JP7635278B2 - OTA TRAFFIC ANALYSIS DEVICE AND OTA TRAFFIC ANALYSIS METHOD - Google Patents

Description

The present invention relates to an OTA (Over The Air) traffic analysis device and an OTA traffic analysis method that estimate an OTA communication traffic pattern from a received wireless signal and predicts the load fluctuation in an OTA space where multiple wireless terminals transmit and receive wireless signals.

As the next-generation communications standard 5G begins to be put into practical use, consideration is being given to local 5G, which can be flexibly built and used by various entities such as local governments and local companies, in order to meet the wide-ranging needs of 5G.

In order to cope with the further rapid increase in mobile traffic, the application of InBand Full-Duplex (IBFD), a highly efficient frequency utilization technology, is being considered.

IBFD can ideally double the frequency utilization efficiency compared to existing duplex methods, but there is an issue of a lot of new interference occurring, so a control technology is needed to obtain various interference amounts and determine whether IBFD can be applied based on the results. To achieve this, interference monitoring technology is needed to grasp the wireless conditions in time and space of, for example, 5G wireless terminals or terminals of various other standards, and to measure the interference conditions of radio waves emitted into space from multiple terminals at high speed and with high accuracy, and as part of this, a system is needed to estimate the direction of arrival of electromagnetic waves at any point.

Conventional systems that estimate the direction of arrival include, for example, systems that estimate the direction of arrival of electromagnetic waves based on signals received at the same positions of multiple antennas that each receive three orthogonal polarized signals (e.g., Patent Document 1, etc.).

JP 2021-96191 A

In the radio wave arrival direction estimation system described in Patent Document 1, for example, three antennas are moved sequentially to the same measurement position, and it is possible to accurately estimate the arrival direction of the electromagnetic waves at that position using all (three) polarized components received at that same position.

However, conventional direction of arrival estimation systems did not take into consideration the function of analyzing communication traffic, such as the length, interval, and data volume of communication data from an incoming signal received at a given location. For this reason, conventional direction of arrival estimation systems had difficulty in handling the process of receiving a wireless signal at a desired observation point in an OTA environment in which multiple wireless terminals transmit and receive wireless signals to each other, and analyzing communication traffic (OTA traffic) in the OTA environment from the received wireless signal.

In particular, with conventional direction-of-arrival estimation systems, it was extremely difficult to estimate the pattern (OTA communication traffic pattern) of the length, interval, or density of communication data transmitted by the wireless signal, such as protocol data units (PDUs) such as packets and frames, and to predict how the communication traffic pattern would fluctuate.

The present invention has been made to solve these problems, and aims to provide an OTA traffic analysis device and an OTA traffic analysis method that can efficiently estimate the traffic pattern of OTA communication from a received radio signal in the OTA space and can also predict its fluctuations.

In order to solve the above problem, the OTA traffic analysis device according to claim 1 of the present invention is an OTA traffic analysis device (1, 1A) that analyzes communication traffic of wireless communication based on separated signals separated for each signal source (110) that is the wireless terminal from a received signal obtained by intermittently capturing a multi-wave mixed signal in which wireless signals transmitted and received between the base station (B1) and the wireless terminal are mixed in an OTA environment in which wireless communication is performed between the base station (B1) and the wireless terminal, and includes a capture control means (31a) for controlling the signal capture time and reception interval of the multi-wave mixed signal, a capture control means (31b) for selecting the separated signals separated after capture for each component of the same communication, estimating the signal pattern of the selected separated signals for each component of the same communication, and a capture control means (31c) for controlling the reception interval of the separated signals. The system is characterized by comprising a PDU estimation means (43, 43A) that estimates a protocol data unit (PDU) transmitted and received by the wireless communication for each component of the same communication based on a signal pattern of a signal, a traffic pattern estimation means (44) that performs processing to estimate a communication traffic pattern defined by PDU data and interval data between the PDU data based on the result of the estimation of the protocol data unit by the PDU estimation means, and a traffic fluctuation prediction means (45) that performs processing to estimate the amount of data within a predetermined period of the communication traffic pattern based on the result of the estimation of the communication traffic pattern by the traffic pattern estimation means, and predict the subsequent load fluctuation of the communication traffic of the communication traffic pattern from the estimated data amount.

With this configuration, the OTA traffic analysis device according to claim 1 of the present invention can estimate PDUs from radio signals received in the OTA space, and efficiently estimate communication traffic patterns in OTA communications from the PDU estimation results, as well as predict subsequent communication traffic load fluctuations.

The OTA traffic analysis device according to claim 2 of the present invention may further include a setting means (41) for setting parameters related to the analysis of the communication traffic, and the setting means may set the values of each item of a PDU length, which is the length of the PDU, a gap, which is the interval between two consecutive PDUs, and a segment, which is a plurality of consecutive elements, as the parameters related to the process of estimating the communication traffic pattern, and the traffic pattern estimation means may be configured to select the sequence of consecutive PDUs estimated by the PDU estimation means in an array in which each item satisfies the respective set value, and extract it as the communication traffic pattern.

With this configuration, the OTA traffic analysis device according to claim 2 of the present invention can easily and efficiently extract a communication traffic pattern of an array having values of PDU length, gap, element, and segment that correspond to the set values of each of the items based on the PDU estimation result by setting parameters for each of the items: PDU length, gap, element, and segment.

In the OTA traffic analysis device according to claim 3 of the present invention, the setting means may further set a shift width, which is an item indicating the number of elements to be moved to the next segment, as the parameter related to the process of estimating the communication traffic pattern, and the traffic pattern estimation means may be configured to extract the sequence of the PDUs estimated by the PDU estimation means multiple times with a time shift equivalent to the shift width.

With this configuration, the OTA traffic analysis device according to claim 3 of the present invention can improve the estimation accuracy by narrowing the shift width, but at the cost of increased processing load, while increasing the shift width reduces the estimation accuracy but reduces the processing load. This makes it possible to selectively set a desirable shift width in terms of both estimation accuracy and processing load, and to carry out the estimation process of the communication traffic pattern.

Furthermore, in the OTA traffic analysis device according to claim 4 of the present invention, the setting means may be configured to set values of each of the following items as the parameters related to the process of estimating the communication traffic pattern: a segment length which is the number of elements in one of the segments; a PDU length limit which is information indicating that all of the PDUs longer than the segment length are classified into a longest PDU length attribute; a number of PDU elements which is information specifying how many divisions the PDUs are to be classified into for the PDUs that do not exceed the PDU length limit; a gap length limit which is information indicating that all of the gaps longer than the segment length and that do not exceed a maximum gap length are to be classified into a longest gap length attribute; a number of gap elements which is information specifying how many divisions the gaps are to be classified into for the gaps that do not exceed the gap length limit; and a maximum gap length which is a maximum limit value for recognizing a gap length.

With this configuration, the OTA traffic analysis device according to claim 4 of the present invention is capable of estimating communication traffic patterns based on further detailed parameter settings including the items of segment length, PDU length limit, number of PDU elements, gap length limit, number of gap elements, and maximum gap length, which is expected to improve the accuracy of estimating communication traffic patterns.

In the OTA traffic analysis device according to claim 5 of the present invention, the setting means may set a monitoring period for aggregating load fluctuations in the communication traffic pattern as the parameter related to the process of estimating the communication traffic pattern, and the traffic fluctuation prediction means may be configured to estimate the amount of data during the set monitoring period.

With this configuration, the OTA traffic analysis device according to claim 5 of the present invention can aggregate the amount of data during a monitoring period set by the user for the communication traffic pattern estimated by the traffic pattern estimation means, and can predict traffic fluctuations based on user-driven settings.

In the OTA traffic analysis device according to claim 6 of the present invention, the setting means may selectively set a monitoring period of short-term, medium-term, or long-term, in that order, and the traffic fluctuation prediction means may estimate the data volume during any one of the set short-term, medium-term, or long-term monitoring periods.

With this configuration, the OTA traffic analysis device according to claim 6 of the present invention can estimate the communication traffic load fluctuation for the short-term, medium-term, or long-term monitoring period desired by the user for the communication traffic pattern estimated by the traffic pattern estimation means, and can smoothly predict subsequent traffic fluctuations according to the length of each monitoring period.

In order to solve the above problem, the OTA traffic analysis method according to claim 7 of the present invention is an OTA traffic analysis method for analyzing communication traffic of wireless communication based on separated signals separated for each signal source (110) that is the wireless terminal from a received signal obtained by intermittently capturing a multi-wave mixed signal in which wireless signals transmitted and received between a base station (B1) and a plurality of wireless terminals (A1) are mixed in an OTA environment in which wireless communication is performed between the base station and the wireless terminals, the method comprising the steps of: selecting the separated signals separated after the capture for each component of the same communication, and estimating the signal pattern of the selected separated signals for each component of the same communication (S61 to S66); and estimating the signal pattern of the selected separated signals for each component of the same communication based on the estimated signal pattern of the separated signals. The method includes a PDU estimation step (S7) for estimating a protocol data unit (PDU) transmitted and received by the wireless communication for each component of the communication, a traffic pattern estimation step (S8) for estimating a communication traffic pattern defined by PDU data and interval data between the PDU data based on the result of estimating the protocol data unit by the PDU estimation step, and a traffic fluctuation prediction step (S9) for estimating the amount of data within a predetermined period of the communication traffic pattern based on the result of estimating the communication traffic pattern by the traffic pattern estimation step, and predicting the subsequent load fluctuation of the communication traffic of the communication traffic pattern from the estimated data amount.

With this configuration, the OTA traffic analysis method according to claim 7 of the present invention, when applied to an OTA traffic analysis device having the configurations described in claims 1 to 6, can estimate PDUs from radio signals received in the OTA space, and efficiently estimate communication traffic patterns in OTA communications from the PDU estimation results, and can also predict subsequent communication traffic load fluctuations.

The present invention provides an OTA traffic analysis device and an OTA traffic analysis method that can efficiently estimate traffic patterns of OTA communications from received radio signals in the OTA space and can also predict fluctuations in the traffic patterns.

1 is a conceptual diagram for explaining a wireless environment in an OTA space in which an OTA traffic analysis device according to the present invention is arranged. FIG. 1 shows an example of the structure of a radio frame for transmitting and receiving a PDU to be analyzed by the OTA traffic analysis device of the present invention, where (a) shows the structure of the entire radio frame, and (b) to (f) show the structure of slots each having a different number of symbols. FIG. 13 is a timing chart showing the PDU estimation process by the OTA traffic analysis device of the present invention, in which (a) to (f) show each PDU pattern to be estimated, (g) shows a multi-wave mixed signal containing a mixture of radio signals transmitting and receiving each PDU pattern, (h) shows a received signal obtained by intermittently capturing the multi-wave mixed signal, (i) shows a separated signal separated for each signal source from the received signal, and (j) shows a timing chart of an estimated PDU estimated from the separated signal. 5A and 5B are timing charts showing a method for capturing a mixed signal of multiple waves in the OTA traffic analysis device according to the present invention, in which FIG. 5A shows an example of a sequential processing method, and FIG. 5B shows an example of a pipeline processing method. FIG. 13 is a timing chart showing the PDU estimation process performed by the OTA traffic analysis device of the present invention, taking into account the estimated results of the arrival direction and signal strength of a separated signal from a received signal, in which (a) shows the signal capture time, (b) to (g) show the respective separated signals, and (h), (i), and (j) show the timing charts of each piece of data that has each separated signal as a component. 4 is a timing chart showing an example of the relationship between the size of a PDU to be analyzed by the OTA traffic analysis device according to the present invention and a signal capture interval. 1 is a block diagram showing a functional configuration of an OTA traffic analysis device according to a first embodiment of the present invention. 5 is a flowchart showing a PDU estimation processing operation in a PDU estimation unit of the OTA traffic analysis device according to the first embodiment of the present invention. 9 is a flowchart showing a procedure of a demultiplexed signal pattern determination process for estimating a PDU for each set of demultiplexed signals selected as components of the same communication in step S7 of FIG. 8 . 10A, 10B, 10C, and 10D show an example of the correspondence between a received signal, a separated signal pattern, and an estimated PDU in the PDU estimation process of the OTA traffic analysis device of the first embodiment of the present invention, in which (a), (b), (c), and (d) respectively show the relationship between each separated signal that is received at different times and is no longer received at different times, and an estimated PDU estimated according to the judgment result by the judgment process procedure shown in FIG. FIG. 2 is a functional block diagram for traffic pattern estimation and traffic fluctuation prediction processing of the OTA traffic analysis device according to the first embodiment of the present invention. 1 is a diagram showing an example of a communication sequence for explaining parameters related to a traffic pattern estimation process of an OTA traffic analysis device according to a first embodiment of the present invention. FIG. 1 is a diagram showing a relationship between a parameter setting example and a data area related to a traffic pattern estimation process of an OTA traffic analysis device according to a first embodiment of the present invention. FIG. 3 is a table showing an example of the configuration of an element definition table used in the traffic pattern estimation process of the OTA traffic analysis device according to the first embodiment of the present invention. FIG. 1A and 1B are diagrams for explaining management of segment information related to the traffic pattern estimation process of an OTA traffic analysis device according to a first embodiment of the present invention, in which (a) shows an example of the configuration of a segment definition table, and (b) is a diagram showing an example of a communication sequence for which the segment definition table of (a) is configured. 3 is a table showing an example of the configuration of a traffic pattern table used in the traffic pattern estimation process of the OTA traffic analysis device according to the first embodiment of the present invention. FIG. 5 is a flowchart showing a traffic pattern estimation processing operation in a traffic pattern estimation unit of the OTA traffic analysis device according to the first embodiment of the present invention. 5 is a flowchart showing a traffic fluctuation prediction processing operation in a traffic fluctuation prediction unit of the OTA traffic analysis device according to the first embodiment of the present invention. 11 is a diagram showing another example of the configuration of a communication sequence that is a target of the traffic pattern estimation process of the OTA traffic analysis device according to the first embodiment of the present invention. FIG. 20A and 20B are diagrams illustrating examples of information handled in a traffic pattern estimation process for the communication sequence shown in FIG. 19 by the OTA traffic analysis device according to the first embodiment of the present invention, in which (a) shows an example of parameter settings, (b) shows an example of the type and size of each element, and (c) shows an example of the configuration of each segment. 20A and 20B are diagrams illustrating tables used in a traffic pattern estimation process for the communication sequence shown in FIG. 19 of the OTA traffic analysis device according to the first embodiment of the present invention, in which (a) shows an example of the configuration of an element definition table, and (b) shows an example of the configuration of a segment definition table. FIG. 11 is a block diagram showing a functional configuration of an OTA traffic analysis device according to a second embodiment of the present invention. 10 is a flowchart showing a PDU estimation process operation in a PDU estimation unit of an OTA traffic analysis device according to a second embodiment of the present invention. FIG. 11 is a timing chart showing PDU estimation processing by an OTA traffic analysis device according to a second embodiment of the present invention, in which (a) shows intermittently performed signal reception, (b) shows signal processing for intermittently received signals, (c) shows separated signals separated for each signal source by signal processing, (d) shows PDU estimation processing based on the separated signals, and (e) shows a timing chart of an estimated PDU obtained by the PDU estimation processing.

Below, an embodiment of the OTA traffic analysis device and the OTA traffic analysis method according to the present invention will be described with reference to the drawings.

(overview)
The OTA traffic analysis device 1 according to the present invention (here, the OTA traffic analysis device 1 according to the first embodiment is illustrated, but the same is true for the OTA traffic analysis device 1A according to the second embodiment) is arranged and operated in an OTA space in which wireless communication is performed between a wireless base station B1 (or an exchange B2) and various wireless terminals A11, A12, A13, and A14, as shown in FIG. 1. The wireless terminals A11, A12, A13, and A14 transmit and receive wireless signals (multiplexed by OFDM, converted into RF signals, and sent out. Hereinafter, they may be referred to as OFDM signals) with the base station B1, and can communicate with a communication partner via the base station B1. Here, the wireless terminals A11, A12, A13, and A14 can communicate with a communication terminal in a network connected to the base station B1, for example, via the base station B1. The wireless terminals A11, A12, A13, and A14 can also communicate with other wireless terminals via the base station B1. In FIG. 1, wireless terminals A11, A12, A13, and A14 (hereinafter, these may be collectively referred to as wireless terminal A1) include various wireless communication devices such as tablet terminals, smartphones, personal computers (PCs), wireless routers, etc.

In the above-mentioned OTA environment, when the base station B1 and multiple wireless terminals A1 simultaneously transmit and receive wireless signals (OFDM signals), a wireless communication situation occurs in which these wireless signals are mixed. The OTA traffic analysis device 1 according to the present invention has an arrival direction/signal strength estimation function (corresponding to the arrival direction estimation processing unit 24 and signal analysis unit 25 described later) that receives a multi-wave mixed signal containing a mixture of wireless signals (OFDM signals) sent from each wireless terminal A1 as a signal source 110 at an appropriate placement point within the observation area 6 during communication between the base station B1 and the wireless terminal A1, separates each mixed wireless signal from the multi-wave mixed signal, and estimates the arrival direction and signal strength for each separated signal (separated signal), i.e., for each signal source 110.

The OTA traffic analysis device 1 according to the present invention further has an OTA traffic analysis function that analyzes communication traffic related to communication between the base station B1 and the wireless terminal A1 based on, for example, the result of estimating the signal arrival direction and signal strength for each signal source 110 by the arrival direction/signal strength estimation function. In particular, the OTA traffic analysis function in the present invention is assumed to include a data estimation function that estimates the interval and length of communication data transmitted and received by communication between the base station B1 and the wireless terminal A1, a traffic pattern estimation function that estimates the communication traffic pattern of OTA communication based on the data estimation result by the data estimation function, and a traffic fluctuation prediction function that aggregates and analyzes the communication traffic pattern estimated by the traffic pattern estimation function to predict the subsequent load fluctuation of communication traffic.

<Data estimation function>
In the present invention, for example, a wireless frame having a structure shown in FIG. 2 is used for transmitting and receiving communication data between the base station B1 and a plurality of wireless terminals A1.

As an example, FIG. 2 shows a configuration example of a radio frame used in IBFD communication of the 5G NR standard. As shown in FIG. 2(a), the radio frame has a length of, for example, 10 milliseconds (ms) and is composed of 10 subframes of equal time length, for example, 1 ms. There are subframes in which one slot is composed of one set of 14 symbols (see FIG. 2(b)), two sets of 14 symbols (see FIG. 2(c)), four sets of 14 symbols (see FIG. 2(d)), eight sets of 14 symbols (see FIG. 2(e)), and 16 sets of 14 symbols (see FIG. 2(f)). The symbol configuration is, for example, one set of 14 symbols for low data rate operation and 16 sets of 14 symbols for high data rate operation.

In 5G IBFD communication, for example, packet data is transmitted and received between the base station B1 and the wireless terminal A1 using the wireless frame shown in FIG. 2. Examples of data types transmitted and received using packet data include email, data, voice, images, and videos. During wireless communication between the base station B1 and the wireless terminal A1, the transmitting side samples, quantizes, encodes, encrypts, and compresses the data to be transmitted based on the protocol specifications, packetizes the data, assembles it into a PDU, and transmits it as a wireless signal after modulation. On the other hand, the receiving side receives the wireless signal sent from the transmitting side, demodulates it, reconstructs the PDU based on the protocol specifications, and then expands and decodes it to reproduce the original data.

When various data are wirelessly transmitted between the base station B1 and the wireless terminal A1 using the above-mentioned wireless frames, the OTA traffic analysis device 1 according to the present invention intermittently captures a multi-wave mixed signal, which is a mixture of wireless signals transmitted and received between the base station B1 and the wireless terminal A1 during communication, for a predetermined time period at predetermined intervals, and performs processing to separate signals for each signal source 110 mixed therein from the multi-wave mixed signal. The reason for intermittently receiving and processing the multi-wave mixed signal is that, from the viewpoint of the signal processing capability of the data processing device 30 (see FIG. 7), it is highly likely that the existing processing capability will not be able to handle constantly receiving and processing a multi-wave mixed signal, which is a mixture of wireless signals sent from multiple wireless terminals, and this is a measure to avoid this as much as possible.

One of the factors that hinders the above-mentioned operation is, for example, signal processing capacity. In the above-mentioned conventional direction-of-arrival estimation system, in order to analyze communication traffic (OTA traffic) in an OTA environment, it is highly likely that the existing processing capacity is not sufficient to constantly receive and process a multi-wave mixed signal that is a mixture of wireless signals sent from multiple wireless terminals. If intermittent reception processing of the wireless signals is performed to avoid this, there is a problem that the OTA traffic cannot be analyzed efficiently and the analysis accuracy is reduced. In addition, there is a risk that the observation may affect the wireless communication system in operation.

Referring to FIG. 3, communication between base station B1 and wireless terminal A1 in observation area 6 can be assumed to involve, for example, the transmission and reception of signals having PDU patterns #1 to #6 as shown in FIG. 3(a) to (f). During communication of signals having PDU patterns #1 to #6, an OTA traffic analysis device 1 placed at a certain point in observation area 6 can receive a transmission signal (multiple-wave mixed signal) in which signals having PDU patterns #1 to #6 are mixed, as shown in FIG. 3(g).

In such an OTA environment, the OTA traffic analysis device 1 repeatedly captures a multi-wave mixed signal for a reception time (signal capture time) Trx (Trx1, Trx2, Trx3, ...) at each reception interval Tint (Tint1, 2, 3, ...), as shown in Figure 3 (h), for example.

As a method for capturing a signal containing multiple waves, for example, a sequential processing method and a pipeline processing method can be applied. The sequential processing method is a method in which capture is performed sequentially at an appropriate time interval, as shown in FIG. 4(a), for example. When the capture accuracy of a signal containing multiple waves is not important, it is possible to reduce the processing load by setting the time interval wider. The pipeline method is a method in which the time interval between captures is shorter than that of the sequential processing method, as shown in FIG. 4(b), and the total capture time is longer than that of the sequential processing method. By setting the time interval to an appropriate value, the processing load increases, but the capture accuracy can be improved compared to the sequential processing method. In the present invention, both the sequential processing method and the pipeline method can be applied, but the following description is based on the assumption that the sequential processing method is applied.

In accordance with the capture of a multi-wave mixed signal (transmission signal) by the sequential processing method shown in FIG. 3(h), the OTA traffic analysis device 1 according to the present invention repeatedly performs a signal separation process to separate signals for each signal source 110 mixed in the received signal (multiple-wave mixed signal) and a process to acquire a signal (separated signal) separated by the signal separation process, as shown in FIG. 3(i), each time it finishes receiving a multi-wave mixed signal at a reception interval Tint1, 2, 3, .... In FIG. 3(i), the processing time Tp of the signal separation process performed sequentially is represented by Tp1, Tp2, Tp3, ..., and the time Ts of the acquired separated signal is represented by Ts1, Ts2, Ts3, ....

Furthermore, as shown in FIG. 3(j), the OTA traffic analysis device 1 according to the present invention performs a PDU estimation process to estimate the pattern of PDUs transmitted and received in the forms shown in FIG. 3(a) to FIG. 3(f) based on the separated signals of time lengths Ts1, Ts2, Ts3, ... obtained by the signal separation process shown in FIG. 3(i). In data communication, a PDU is a unit of transmission and reception of data bundled according to a protocol (communication rules) specification, and an example is a packet. From the viewpoint of OTA traffic analysis accuracy, it is desirable that the PDU estimated by the PDU estimation process shown in FIG. 3(j) (estimated PDU) is as close as possible to PDU patterns #1 to #6 shown in FIG. 3(a) to FIG. 3(f).

In this way, the OTA traffic analysis device 1 according to the present invention has, as the above-mentioned data estimation function, a PDU estimation processing function that estimates a PDU based on a separated signal separated for each signal source 110 from an intermittently captured multi-wave mixed signal.

<PDU estimation processing function>
The PDU estimation processing function selects some of the constituent (fragment) elements (corresponding to the communication data received at capture timings Trx1, Trx2, Trx3, ...) of PDU patterns #1 to #6 (see Figures 3 (a) to (f)) transmitted and received by the ongoing wireless communication for each component of the same communication, and performs PDU estimation processing to recognize an estimated PDU for each same communication from the selected fragment elements for each component of the same communication.

The various types of data (email, data, voice, images, videos, etc.) transmitted and received between base station B1 and wireless terminal A1 described above can be roughly inferred from the data type, such as the length (maximum size, minimum size) and interval (gap between PDUs) of the PDU when transmitted and received in a wireless frame (see Figure 2), or the data rate, etc. In other words, if the type of communication data wirelessly communicated between base station B1 and wireless terminal A1 is known to some extent, even if the communication data is received intermittently, the length and interval of the entire PDU can be estimated from the intermittently received received signal.

As an example, the PDU estimation process performed in association with the estimated results of the direction of arrival and signal strength of the separated signals will be described with reference to FIG. 5. Note that for the capture implementation timing shown in FIG. 5(a), the capture start times (times corresponding to the start points of the reception times Trx1, Trx2, Trx3, ... in FIG. 3(h)) are represented as t1, t2, t3, .... Also, for the separated signals shown in FIG. 5(b) to (g), the signal processing times after the capture start times t1, t2, t3, ... (corresponding to the signal separation processing time Tp shown in FIG. 3(i)) are omitted, and each is associated with the estimated results of the direction of arrival and signal strength.

When estimating a PDU from a separated signal (see FIG. 5 (b) to (g)) associated with the estimated results of the direction of arrival and signal strength, the OTA traffic analysis device 1 according to the present invention estimates the directions of arrival A, B, C, A', B', C', ... and signal strengths of the signals (separated signals) Sa, Sb, Sc, Sa', Sb', Sc', ... separated for each signal source 110 from the multi-wave mixed signal received during communication between the base station B1 and the wireless terminal A1, and stores the separated signals Sa, Sb, Sc, Sa', Sb', Sc', ... together with the estimated directions of arrival A, B, C, A', B', C', ... and signal strengths in the database 33 (see FIG. 7) in association with the capture start times t1, t2, t3, ....

In conjunction with this process, the PDU estimation processing function continuously searches the stored separated signals Sa, Sb, Sc, Sa', Sb', Sc', ... and their arrival directions A, B, C, A', B', C', ... and the estimated signal strengths for each capture start time t1, t2, t3, ... (see Figures 5 (a) to (g)), and selects components of the same communication from each of the multiple searched separated signals Sa, Sb, Sc, Sa', Sb', Sc', .... The arrival direction is referenced as a parameter for selecting components of the same communication.

Specifically, when capture proceeds in the direction from time t1 to time t7 in FIG. 5(a), of the separated signals Sa, Sb, Sc, Sa', Sb', Sc', ... shown in FIG. 5(b) to (g), for the separated signals Sa, Sb, and Sc that are received and separated at time t1, directions of arrival A, B, and C are captured, respectively.

Similarly, for the separated separated signals Sb and Sa' received at time t2, the directions of arrival B and A' are captured, respectively; for the separated separated signals Sc, Sa' and Sb' received at time t3, the directions of arrival C, A' and B' are captured, respectively; for the separated separated signals Sa, Sb' and Sc' received at time t4, the directions of arrival A, B' and C' are captured, respectively; for the separated separated signals Sa, Sb and Sc' received at time t5, the directions of arrival A, B and C' are captured, respectively; for the separated separated signals Sc received at time t6, the direction of arrival C' is captured; and for the separated separated signals Sa', Sb' and Sc' received at time t7, the directions of arrival A', B' and C' are captured, respectively.

Here, for example, if the angle difference between the directions (angles) of arrival direction A and arrival direction A' is within a preset threshold (threshold angle), it can be inferred that separation signal Sa from arrival direction A and separation signal Sa' from arrival direction A' are signals arriving from the same signal source 110 (components of the same communication). This inference method also applies to separation signal Sb from arrival direction B and separation signal Sb' from arrival direction B', and separation signal Sc from arrival direction C and separation signal Sc' from arrival direction C'.

In other words, in the OTA traffic analysis device 1 according to the present invention, a threshold angle is set in advance, and as the multiple wave mixed signal is captured and separated in the order of time t1, t3, t4, t5, t6, t7, ... at the timing shown in FIG. 5(a), the separated signals before and after whose estimated direction of arrival is within the range of the threshold angle can be extracted as components of the same communication.

Specifically, in the examples of Figures 5(b) to (g), separated signals Sa and Sa' can be extracted as components of the same communication. Similarly, separated signals Sb and Sb', and separated signals Sc and Sc' can be extracted as components of other identical communications. Note that in Figures 5(b) to (g), the blank areas corresponding to times t1, t3, t4, t5, t6, t7, ... indicate a state in which a separated signal does not exist, and the areas indicated by dotted lines indicate a state in which a separated signal could not be received for some reason (a state of reception failure).

According to the results of the selection of components of the same communication based on the threshold angle described above, separated signals Sa and Sa' are selected as the same communication element in succession, except for time t6, among times t1, t3, t4, t5, t6, t7, ... in FIG. 5(a). Separation signals Sb and Sb' are selected as another identical communication element in succession, except for time t6 (reception miss). Separation signals Sc and Sc' are selected as yet another identical communication element in succession, except for time t2 (reception miss).

In addition, in FIG. 5, (h), (i), and (j) show an example of a transmission/reception pattern of packet data A, B, and C transmitted and received between base station B1 and wireless terminal A1 in observation area 6 using a wireless frame having the configuration shown in FIG. 2. The section of time width t0 shown in a dotted line frame in FIG. 5 (h), (i), and (j) corresponds to the intermittent capture section spanning time t1 to t7 shown in FIG. 5 (a).

According to the timing chart shown in FIG. 5, in the OTA traffic analysis device 1 according to the present invention, a multi-wave mixed signal containing packet data A, B, and C transmitted and received between base station B1 and wireless terminal A1 is intermittently captured over a period wider than time width t0, fragments (separated signals) of each component of packet data A, B, and C are extracted, and these separated signals are selected for each component of the same communication, making it possible to estimate the interval and length of packet data A, B, and C (PDU) that includes the separated signal as part of it.

Here, in order to estimate the interval and length of the PDU after selecting a separated signal for each component of the same communication, it is necessary to estimate for each selected separated signal whether the separated signal is the beginning, end, or continuation part of the PDU.

The reason for this will be explained with reference to Figure 6. The size of the PDUs that are the subject of OTA traffic analysis by the OTA traffic analysis device 1 according to the present invention and the signal capture interval have the relationship shown in Figure 6, as an example.

As shown in the upper part of Figure 6, PDUs #1, #2, #3, ... can be sent appropriately at different intervals (g1, g2, ...) and different lengths of time. On the other hand, in the OTA traffic analysis device 1 according to the present invention, as shown in the lower part of Figure 6, a multi-wave mixed signal containing PDUs #1, #2, #3, ... is intermittently received for a certain length of time at regular intervals i1, i2, i3, i4, ... and separated for each signal source 110, and the separated signals are selected for each component of the same communication to extract PDUs #1, #2, #3, ....

As can be seen from the relationship shown in Figure 6, reception of PDU#1 begins almost at the beginning of capture interval i1, but is not received during capture interval i2. Reception of PDU#2 begins almost at the beginning of capture interval i3, and after being received at the rear end, it is no longer received. Reception of PDU#3 begins at the beginning of capture interval i5, a short time after the beginning, and continues to be received at the rear end.

In this way, in order to capture PDUs #1, #2, #3, ... of different lengths and intervals at regular intervals i1, i2, i3, i4, ... and estimate the length as accurately as possible, it is useful to estimate for each separated signal of the selected components of the same communication whether the separated signal is the beginning, continuation, or end of the PDU, which also improves the accuracy of the estimated PDU. The process procedure for determining whether a separated signal is the beginning, continuation, or end of a PDU will be explained in detail later with reference to Figure 9.

Note that in FIG. 5, the PDU estimation process takes into account (associates) the arrival direction and signal strength of the separated signal separated from the received multi-wave mixed signal. However, in the present invention, it is not necessarily necessary to associate the separated signal with the arrival direction and signal strength, and the PDU may be estimated based on the separated signal alone. This configuration will be described in detail later as the second embodiment.

<Traffic pattern estimation function>
This is a function to estimate the traffic pattern (communication traffic pattern) of OTA communication based on the estimation result of communication data by the data estimation function, specifically, the estimation result of PDU by the PDU estimation unit 43.
<Traffic fluctuation prediction function>
This function estimates the amount of data in a given period of time for a communication traffic pattern based on the results of the communication traffic pattern estimation function, and predicts the subsequent communication traffic load fluctuation of the communication traffic pattern from the estimated data amount.

First Embodiment
Based on the above-mentioned overview, the configuration of the OTA traffic analysis device 1 according to the first embodiment of the present invention will be described below with reference to Fig. 7. Note that the first embodiment is premised on estimating a PDU from a separated signal associated with the estimated results of the arrival direction and signal strength.

As shown in FIG. 7, the OTA traffic analysis device 1 according to this embodiment is configured with an antenna device 10, a blind signal estimation device 20, and a data processing device 30.

The antenna device 10 receives a multi-wave mixed signal transmitted from multiple signal sources 110 within an observation area 6 (see FIG. 1). Specific configurations of the antenna device 10 include, for example, a configuration in which three antennas capable of receiving three orthogonal polarized waves are rotated on a rotating body as multiple antenna elements as described in Patent Document 1, or a configuration in which each antenna element of an array antenna is configured as multiple antenna elements.

The OTA traffic analysis device 1 of this embodiment receives incoming signals from the surrounding wireless environment, i.e., a multi-wave mixed signal that is a mixture of signals transmitted from multiple signal sources 110 within the observation area 6, using multiple antenna elements provided in the antenna device 10, and processes the received signals of each antenna element using the blind signal estimation device 20 and the data processing device 30 to separate the signals transmitted from each signal source 110 and estimate the direction of arrival and signal strength of each signal source 110, and estimate the PDU based on the estimated results of the direction of arrival and signal strength.

The OTA traffic analysis device 1 according to this embodiment can be used, for example, in a local 5G environment, and the frequency bands of the incoming signals to be captured are assumed to be, for example, 3.75 GHz, 4.6 GHz to 4.8 GHz, and 28.2 GHz to 29.1 GHz. The OTA traffic analysis device 1 is not limited to use in a local 5G environment, and can also be used in other wireless systems such as Wi-Fi.

In the OTA traffic analysis device 1, the antenna device 10 is not limited to the configuration described above. As long as it is possible to comprehensively obtain the direction of arrival and various characteristics for inter-area terminal-to-terminal interference, inter-base station interference, etc. in the above-mentioned frequency bands, various methods can be applied for the type, number, arrangement, driving method, and method of estimating the direction of arrival of the antennas.

As shown in FIG. 3, the blind signal estimation device 20 has a frequency conversion unit 21, an AD conversion unit 22, a signal separation unit 23, an arrival direction estimation processing unit 24, and a signal analysis unit 25.

The frequency conversion unit 21 inputs the received signal (multiple-wave mixed signal) received by the antenna device 10 and converts the received signal into an intermediate frequency band signal (IF signal).

The AD conversion unit 22 converts the received signal, which has been frequency-converted by the frequency conversion unit 21, from an analog signal to a digital signal and outputs the digital signal to the signal separation unit 23. The AD conversion unit 22, together with the antenna device 10 and the frequency conversion unit 21, constitutes the receiving unit 20a.

The signal separation unit 23 performs signal separation processing to separate a signal transmitted from one of the multiple signal sources 110 from the digital signal input from the AD conversion unit 22, i.e., the multiple wave mixed signal received at a specified placement point in the observation area 6.

The arrival direction estimation processing unit 24 inputs the signals (separated signals) separated by the signal separation unit 23, and performs signal processing to estimate the arrival direction of each input signal. In this signal processing, for example, the beamformer method, the MUSIC method, the ESPRIT method, etc. are applied as algorithms for estimating the arrival direction. In these methods, for example, the horizontal axis is the angle and the vertical axis is the signal level, and an angular spectrum is generated that represents the arrival direction (angle) of the arriving signal in relation to the signal level, and the peak of the signal level on the vertical axis of the angular spectrum is read, and the angle on the horizontal axis corresponding to the above peak is estimated as the arrival direction (angle) of the arriving signal having the angular spectrum. The arrival direction estimation processing unit 24 constitutes the arrival direction estimation means of the present invention.

The signal analysis unit 25 inputs the separated signals input from the signal separation unit 23, and performs signal processing to analyze the separated signals for various items. Items to be analyzed (analysis items) include, for example, signal strength. The signal analysis unit 25 corresponds to the signal strength estimation means of the present invention, and constitutes the analysis processing unit 20b together with the above-mentioned signal separation unit 23 and direction of arrival estimation processing unit 24.

The blind signal estimation device 20 is equipped with a frequency conversion unit 21, an AD conversion unit 22, a signal separation unit 23, an arrival direction estimation processing unit 24, and a signal analysis unit 25, and is capable of receiving a multi-wave mixed signal arriving at a placement point within the space of the observation area 6, separating the mixed signals therein, and estimating the arrival direction and signal strength for each separated signal.

The data processing device 30 is realized, for example, by a PC (personal computer), and performs data processing to analyze communication traffic (OTA traffic) carried out between wireless terminals A1 (signal source 110) within the observation area 6 based on the analysis results of the received signal in the blind signal estimation device 20, in particular the estimation results of the signal arrival direction and signal strength. The data processing device 30 is configured with a control unit 31, a database 33, an input unit 34, and a display unit 35.

The control unit 31 is, for example, configured by a computer device. This computer device has a CPU (Central Processing Unit) that performs predetermined information processing to realize the functions of the OTA traffic analysis device 1 and performs overall control of the antenna device 10 and the blind signal estimation device 20, a non-volatile memory such as a ROM (Read Only Memory) or HDD (Hard Disc Drive) that stores an OS (Operating System) for starting up the CPU, other programs, and control parameters, and a RAM (Random Access Memory) that stores the execution code and data of the OS and applications used by the CPU for operation.

The above-mentioned computer device functions as the control unit 31 by the CPU using the RAM as a working area to execute programs stored in a non-volatile memory such as a ROM or HDD. Specifically, the control unit 31 realizes the functions of an analysis condition setting unit 41, an antenna control unit 42, a PDU estimation unit 43, a traffic pattern estimation unit 44, a traffic fluctuation prediction unit 45, and a display control unit 47 as shown in FIG. 7 by the CPU executing each program stored in the non-volatile memory in the working area of the RAM. Here, the analysis condition setting unit 41 and the antenna control unit 42 constitute a functional unit serving as a device control unit 31a that controls the blind signal estimation device 20, and the PDU estimation unit 43, the traffic pattern estimation unit 44, the traffic fluctuation prediction unit 45, and the display control unit 47 constitute a functional unit serving as a data control unit 31b that performs data processing related to OTA traffic analysis.

The analysis condition setting unit 41 is a functional unit for setting the analysis conditions for OTA traffic in the observation area 6, and is capable of setting, for example, the frequency of the radio signal to be analyzed for OTA traffic, and constitutes the setting means of the present invention. The analysis condition setting unit 41 may be configured to set, for example, the 3.7 GHz band, the 4.7 GHz band, or the 28 GHz band as the frequency band to be analyzed for OTA traffic, taking into account the operation of 5G.

The antenna control unit 42 performs mechanical control of the antenna direction, etc., of the antenna device 10.

The device control unit 31a, which has an analysis condition setting unit 41 and an antenna control unit 42, also has a function of controlling the capture time (reception time Trx in FIG. 3(h)) and reception interval (Tint in FIG. 3(h)) of the multiple wave mixed signal. The device control unit 31a constitutes the capture control means of the present invention.

The PDU estimation unit 43 corresponds to the PDU estimation processing function described above, and sequentially stores in the database 33 the signals generated by signal processing in accordance with the intermittent reception (capture) of a multi-wave mixed signal in the blind signal estimation device 20, i.e., the separated signals, and the estimated results of the arrival direction and signal strength of the separated signals, and executes a PDU estimation process to estimate the length and interval of a PDU that includes the separated signals as part of its components, based on the stored separated signals and the estimated results of the arrival direction and signal strength of the separated signals. The PDU estimation unit 43 constitutes the PDU estimation means of the present invention.

The traffic pattern estimation unit 44 corresponds to the traffic pattern estimation function described above, and executes a traffic pattern estimation process to calculate and collect communication traffic patterns (data volume, data density, etc.) near each signal source based on the PDU length, interval, and density from the PDU estimation results by the PDU estimation unit 43. The traffic pattern estimation unit 44 constitutes the traffic pattern estimation means of the present invention.

The traffic fluctuation prediction unit 45 corresponds to the traffic fluctuation prediction function described above, and executes a process of analyzing the trends of the traffic collected by the traffic pattern estimation unit 44 and predicting future traffic fluctuations based on the analysis results. The traffic fluctuation prediction unit 45 constitutes the traffic fluctuation prediction means of the present invention. The configuration of the traffic pattern estimation function and the traffic fluctuation prediction function will be described in detail later with reference to Figures 11 to 16.

The display control unit 47 controls the display of various information and screens related to the OTA traffic analysis process, such as a screen for setting OTA traffic analysis conditions and a display screen for displaying PDU estimation results.

The database 33 is a functional unit for storing various information related to OTA traffic analysis, such as the separated signals output by the signal separation unit 23, the estimated directions of arrival and signal strengths of the separated signals output by the direction of arrival estimation processing unit 24 and the signal analysis unit 25, respectively, the PDU estimation results by the PDU estimation unit 43, the communication traffic patterns (data volume, data density, etc.) calculated and extracted by the traffic pattern estimation unit 44, and the predicted results of traffic fluctuations by the traffic fluctuation prediction unit 45.

The input unit 34 is a functional unit for inputting various information such as commands, and is composed of input devices such as a keyboard and a mouse. In this embodiment, the input unit 34 may have a function (function as an operation unit) for setting various parameters such as the frequency related to the OTA traffic analysis process, and for instructing the start or end of the OTA traffic analysis process.

The display unit 35 displays various information and screens related to the OTA traffic analysis process, such as a setting screen for OTA traffic analysis conditions (setting parameters) and a display screen for displaying PDU estimation results, through the display control of the display control unit 47 described above. The display unit 35 may be configured with a touch panel or the like to enable input of setting parameters, commands, etc.

Next, the PDU estimation process operation in the OTA traffic analysis device 1 according to this embodiment will be described with reference to the flowchart shown in FIG.

To perform this PDU estimation process, the OTA traffic analysis device 1 is installed at a desired location within the observation area 6.

After installation at a desired location, the OTA traffic analysis device 1 performs a pre-start setting (analysis condition setting) process in which the analysis condition setting unit 41 of the control unit 31 sets the setting items (parameters) required to execute the PDU estimation process (step S1). The analysis condition setting process can be performed, for example, by displaying a setting screen on the display unit 35 and accepting user input of setting values from the input unit 34 into the setting fields of each setting item displayed on the setting screen.

The analysis condition setting unit 41 accepts input (setting operations) of setting items such as frequency, data rate, received signal length (signal capture time), reception interval, maximum PDU size, minimum PDU size, minimum gap length between PDUs, and threshold angle (used when selecting components of the same communication from the direction of arrival) based on the protocol used for communication between the base station B1 and the wireless terminal A1 and the configuration of the PDU that matches the protocol specifications, and performs processing to set each of the input items. The received signal length (signal capture time) and reception interval correspond to the reception time Trx and reception interval Tint in FIG. 3, respectively.

Next, the control unit 31 accepts an operation by the user to start the traffic analysis process, for example, at the input unit 34 (step S2). This start operation can be performed, for example, by pressing the "Start" button displayed on the setting screen described above.

When a predetermined start operation for the traffic analysis process is performed, the OTA traffic analysis device 1 controls the antenna device 10 and the blind signal estimation device 20 from the device control unit 31a of the data processing device 30 to receive a multi-wave mixed signal containing a mixture of multiple wireless signals (step S3).

Here, the antenna control unit 42 of the device control unit 31a drives and controls the antenna device 10 so as to intermittently capture the multiple-wave mixed signal of the frequency band set by the analysis condition setting unit 41 at the reception signal length (signal capture time) and reception interval set in step S2. In addition, the device control unit 31a drives and controls the blind signal estimation device 20 so as to perform signal processing of the captured signal in accordance with the intermittent capture of the multiple-wave mixed signal of the frequency band by the antenna device 10. By this drive control, in the blind signal estimation device 20, the reception signal intermittently captured by the antenna device 10 is frequency converted by the frequency conversion unit 21, and the frequency-converted reception signal is subjected to signal processing in which the AD conversion unit 22 converts the analog signal to a digital signal, and the signal is quickly input to the signal separation unit 23 after capture.

The signal separation unit 23 performs a process of separating a plurality of signal source components arriving from the surroundings at the point from the input signal (captured signal) for each signal source 110 (see signal separation time Tp in FIG. 3) (step S4), and inputs the signal source components of the separated signal (separated signal) to the arrival direction estimation processing unit 24 and the signal analysis unit 25. Next, the arrival direction estimation processing unit 24 and the signal analysis unit 25 perform a process of analyzing the input separated signal to estimate the arrival direction and signal strength of the separated signal (see separated signal time Ts ≒ reception time Trx in FIG. 3) (step S5).

The direction of arrival estimation processing unit 24 estimates the direction of arrival in step S5 by applying, for example, the MUSIC method, beamforming method, or the like to the inputted separated signals (signal source components transmitted from any of the multiple signal sources 110) to estimate the direction of arrival for each of the separated signals. The direction of arrival estimation processing unit 24 outputs the result of estimating the direction of arrival of the signal source 110 to the data processing device 30. Also, in step S5, the signal analysis unit 25 calculates the signal strength for each inputted separated signal, and also performs analysis processing on other items, and outputs the calculated signal strength for each separated signal and the analysis results of the other items to the data processing device 30.

Specifically, when applying the MUSIC method, for example, the direction of arrival estimation processing unit 24 generates a MUSIC angle spectrum for each signal source component input from the signal separation unit 23. The MUSIC angle spectrum is, for example, configured as a graph with the horizontal axis representing the angle and the vertical axis representing the signal level, which represents the direction of arrival (angle) of the incoming signal in relation to its signal level.

Next, the arrival direction estimation processing unit 24 reads the signal level peak on the vertical axis of the MUSIC angle spectrum, and estimates the angle on the horizontal axis corresponding to the peak as the arrival direction (angle) of the incoming signal having that angle spectrum.

In contrast, when the beamforming method is applied, the direction-of-arrival estimation processing unit 24 generates a beamforming angle spectrum for each signal source component input from the signal separation unit 23. The beamforming angle spectrum is also configured as a graph, for example, with the horizontal axis representing the angle and the vertical axis representing the signal level, which represents the direction of arrival (angle) of the incoming signal in relation to its signal level.

As a result, the arrival direction estimation processing unit 24 reads the signal level peak on the vertical axis of the beamforming angle spectrum, and estimates the angle on the horizontal axis corresponding to the peak as the arrival direction (angle) of the incoming signal having that angle spectrum.

The separated signals separated in step S4 and the estimated direction of arrival and signal strength for each separated signal based on the analysis process in step S5 are continuously recorded in a specified storage area of the database 33 by the data control unit 31b (step S6).

In line with this, the PDU estimation unit 43 performs a process of estimating the size of the PDU that includes the separated signals stored in step S6 as part of its components and is transmitted and received between the base station B1 and the wireless terminal A1, based on the estimation results of the arrival direction and signal strength of each separated signal. (step S7)

The PDU estimation process in step S7 can be performed, for example, by the procedure according to the timing chart shown in FIG. 5, as described above. Here, the PDU estimation unit 43 uses the estimated results of the arrival direction and signal strength of the multiple separated signals stored corresponding to each capture time (see step S6) as indicators, and continuously performs a process of selecting, as components of the same communication, for each pair of separated signals that satisfy a preset condition before and after the capture time, i.e., the condition of the range of the threshold angle set in step S2 above, as components of the same communication, as the capture time progresses.

In this process, the separated signals selected as components of the same communication (in the example of FIG. 5, Sa and Sa', Sb and Sb', Sc and Sc') may appear in a mixture of various patterns, such as a first pattern where a pair is not selected at a certain capture time but is selected at the next capture time, a pattern where the pair continues to be selected for multiple capture times after a certain capture time (second pattern), and a pattern where the pair is selected at a certain capture time but is no longer selected at the next capture time (third pattern).

Assuming that such patterns of separation signals (separation signal patterns) appear, for example, the separation signal of the first pattern described above can be inferred to be the separation signal of the beginning part of the PDU to be estimated (estimated PDU). Furthermore, the separation signal of the second pattern and the separation signal of the third pattern described above can be inferred to be the separation signal of the continuing part (continuation part) and the separation signal of the end part, respectively, of the estimated PDU.

An example of the correspondence between received signals, separated signal patterns, and estimated PDUs is shown in FIG. 10. In FIG. 10, (a) shows the correspondence when only a separated signal pattern that is no longer received at the timing next to the received signal is included, (b) shows the correspondence when a separated signal pattern that is no longer received at the timing next to the received signal and two separated signal patterns that are no longer received at the timing next to the received signal are included, (c) shows the correspondence when a separated signal pattern that is no longer received at the timing next to the received signal and a separated signal pattern that is no longer received four timings after the received signal are included, and (d) shows the correspondence when a separated signal pattern that is no longer received at the timing next to the received signal and a separated signal pattern that is no longer received at the timing next to the received signal after two consecutive timings of no reception are included.

According to the correspondence between the received signal, separated signal pattern, and estimated PDU shown in FIG. 10, as shown in (a) to (d) of FIG. 10, from a separated signal pattern that is no longer received at the next timing after it was received, it is possible to estimate (obtain an estimated PDU) the PDU that is the beginning of the separated signal and continues from the received capture time to just before the start of the next capture time.

Also, as shown in Figures 10(b) and (d), from a separated signal pattern that is no longer received at the next timing after it was received, it is possible to obtain an estimated PDU that continues from the time it was received until just before the start of the two subsequent capture times.

Also, as shown in FIG. 10(c), for a separated signal that is no longer received four capture times after it was received, it is possible to obtain an estimated PDU that continues until the start of the capture time four capture times after it was received.

The PDU estimation process using the relationship between the separated signal pattern and the estimated PDU shown in Fig. 10 will be described in detail with reference to Fig. 9. In the PDU estimation process in step S7 in Fig. 8, the PDU estimation unit 43 sequentially reads out the estimated results of the arrival direction and signal strength of the stored separated signals (see step S6), and performs a process of determining the pattern of the separated signals (separated signal pattern) for each set of separated signals selected as components of the same communication, i.e., for each arrangement of separated signals for each signal source 110, according to the flowchart shown in Fig. 9.

Here, the PDU estimation unit 43 determines, for the separated signal at each capture time for each signal source 110, for example, for a certain signal source 110, that the point at which the separated signal was detected is the beginning of an estimated PDU (estimated PDU) (step S61).

Furthermore, if the separation signal is still detected, the PDU estimation unit 43 determines, for example, that the estimated PDU is continuing (step S62).

In contrast, if a separation signal is not detected, the PDU estimation unit 43 determines, for example, that this is the end of the estimated PDU at that point (step S63).

While repeatedly performing the processes from step S61 to step S63, the PDU estimation unit 43 sequentially constructs an estimated PDU from the separated signal of the signal sent from the signal source 110. During the process of constructing the estimated PDU, the PDU estimation unit 43 determines that the end of the PDU has been reached when the data length calculated from the elapsed time from the beginning of the PDU and the data rate reaches a preset maximum PDU size (step S64).

On the other hand, if the data length is found to be shorter than the preset minimum PDU size in step S64, the PDU estimation unit 43 extends the data to the minimum PDU size and adjusts the PDU start position and PDU end position (time) (step S65). To perform this process, the PDU estimation unit 43 is able to determine whether it has missed only the start position, only the end position, or both the start and end positions. Depending on the result of this determination, if it has missed only the start position, the PDU estimation unit 43 advances the start position (time) by the amount of the size extension, and if it has missed only the end position, it postpones the end position (time) by the amount of the size extension. If it has missed both the start position and the end position, it performs a process of shifting both the start position and the end position by half the amount of the size extension.

In addition, during the process of constructing an estimated PDU by repeating steps S61 to S63, if the PDU estimation unit 43 detects the next separation signal at an interval shorter than the preset minimum gap between PDUs from the end of the PDU, it determines that the point where the separation was detected is the beginning of the next PDU, and goes back to the previous estimated PDU and modifies it to a size that matches the minimum gap between PDUs (step S66).

As shown in FIG. 9, the PDU estimation unit 43 selects, for each group, separated signals that are selected as components of the same communication from the estimated results of the arrival direction and signal strength of the stored multiple separated signals (see step S6 in FIG. 8), and updates the estimated PDU by applying a separation signal pattern determination process (see FIG. 10) to each separate separated signal in each group.

By this process, in step S7 of FIG. 8, the PDU estimation unit 43 can finally estimate (obtain an estimated PDU) the pattern of the PDU sent by the wireless signal from the signal source 110 for each component of the same communication, i.e., for each signal source 110. In this way, the PDU estimation unit 43 continues the PDU estimation process until an estimated PDU is obtained that matches the frequency, data rate, maximum PDU size, minimum PDU size, and minimum inter-PDU gap length set in step S1 of FIG. 8.

Next, we will explain the traffic pattern estimation function and traffic fluctuation prediction function of the OTA traffic analysis device 1 according to this embodiment.

As shown in FIG. 7, in the OTA traffic analysis device 1 according to this embodiment, the data control unit 31b of the data processing device 30 is provided with a traffic pattern estimation unit 44 and a traffic fluctuation prediction unit 45 in addition to the PDU estimation unit 43.

The traffic pattern estimation unit 44 realizes a traffic pattern estimation function that extracts the estimated PDU based on the PDU estimation result by the PDU estimation unit 43 using preset extraction conditions (PDU length, PDU interval (gap), number of elements, etc.) and detects (estimates) the PDU that satisfies the extraction conditions as a communication traffic pattern (of a desired type) that corresponds to the extraction conditions.

The traffic fluctuation prediction unit 45 obtains traffic (data volume, data density, etc.) for a predetermined period of time for multiple types of communication traffic patterns estimated by the traffic pattern estimation unit 44, analyzes trends related to fluctuations in the traffic, and, based on the analysis results, realizes a traffic fluctuation prediction function that predicts future traffic fluctuations (after the above-mentioned predetermined period) for the communication traffic pattern.

<Traffic Pattern Estimation Processing>
An overview of the traffic pattern estimation process by the traffic pattern estimation function will be described with reference to Fig. 12. In the OTA traffic analysis device 1 according to this embodiment, as an example of a communication sequence between a base station B1 and a wireless terminal A1 performed under an OTA environment (see Fig. 1), for example, a form in which PDUs (shown in a rectangular frame with diagonal lines) are transmitted and received according to the communication sequence shown in Fig. 12 can be assumed. In Fig. 12, P1, P2, P3, P4, P5, P6, and P7 indicate the length of each PDU, and G1, G2, G3, G4, G5, and G6 indicate the interval (gap length) between two consecutive PDUs. The unit of the gap length is time or number of bytes, and it is necessary that it is consistent overall.

In Figure 12, each PDU length (P1, P2, P3, P4, P5, P6, P7) and gap length (G1, G2, G3, G4, G5, G6) is called an element, and several elements are linked together to form multiple consecutive segments. Figure 12 shows an example of an arrangement of three consecutive segments S1, S2, and S3 when one segment is set to five elements. Here, elements (P1, G1, P2, G3, P3) form the segment indicated by S1, followed by elements in the same row (G3, P4, G4, P5, G5) that form the segment indicated by S2, and further elements in the same row (P6, G6, P7, ...) that form the segment indicated by S3.

In addition, in FIG. 12, the arrays in which multiple segments S1, S2, and S3 are shifted backward in element units are vertically arranged as (S2', S3', S4', S5', S6', S7', S8', and S9'). Here, the difference between the tip of segment S2' and the tip of segment S3', the difference between the tip of segment S3' and the tip of segment S4', the difference between the tip of segment S4' and the tip of segment S5', the difference between the tip of segment S5' and the tip of segment S6', and the difference between the tip of segment S6' and the tip of segment S7' are referred to as shift widths. In other words, the shift width refers to the number of elements moved to the next segment when identifying and judging the segments sequentially. As shown in FIG. 12, when shift width = number of elements/segment, it is S1 to S3, and when shift width = 1, it is S1 and S2' to S9'. Here, decreasing the shift width increases the processing load but improves the estimation accuracy, and increasing the shift width decreases the estimation accuracy but reduces the processing load.

When detecting the arrangement of PDUs transmitted and received by the communication sequence illustrated in FIG. 12, it is possible to set parameters such as the PDU length, PDU interval (gap), number of elements, segments, shift width, etc. in advance, and extract a PDU arrangement (communication traffic pattern) that matches the parameter values from the PDU estimation result by the PDU estimation unit 43.

Various parameters such as the PDU length, PDU interval (gap), element, segment, shift width, etc. shown in FIG. 12 can be set to any value by a setting process in the analysis condition setting unit 41 in response to user operation in the input unit 34 (or the display unit 35), for example. As a result, the traffic pattern estimation unit 44, which serves as the traffic pattern estimation function described above, can estimate (extract) a communication traffic pattern defined by the PDU information (PDU data) and gap information (interval data) between consecutive PDU data based on the PDU estimation result after setting the parameters, in parallel with the PDU estimation process in the PDU estimation unit 43, or after reading the processing data (including the PDU estimation result) stored in the processing data storage unit 33a by the PDU estimation process.

In addition, the traffic pattern estimation unit 44 is capable of receiving records including, for example, PDU sender identification information (ID number, character string, etc.), PDU start time (milliseconds or less), PDU length (bytes), and PDU required time (milliseconds or less) information during traffic pattern estimation processing. The sender identification information is unique information within the system for identifying the sender. The PDU start time is the start time of transmission or reception of the beginning of the PDU. The PDU length is the byte length of the PDU. The PDU required time is the time required for transmission of the PDU on the communication path. The above-mentioned records can be received one by one at the time the record is generated, or can be received as status input information that has been saved and accumulated for multiple records generated during a certain time period.

In this embodiment, the parameters required to extract the communication traffic pattern of the desired PDU include the segment length, shift width, PDU length limit, number of PDU elements, gap length limit, number of gap elements, and maximum gap length.

The segment length is the number of elements in one segment. As mentioned above, the shift width is the number of elements to the beginning of the next segment. The PDU length limit is information indicating that all PDUs longer than this are classified as having the "longest" PDU length attribute. The PDU element count specifies how many divisions the element length is classified into for PDUs that do not exceed the PDU length limit, and is a value greater than the PDU length limit.

The gap length limit is information indicating that all gaps longer than this but not exceeding the "maximum gap length" are classified as having the "longest" gap length attribute, and is a value smaller than the maximum gap length. The number of gap elements is information specifying how many divisions the element length is to be classified into for gaps that do not exceed the gap length limit, and is a value larger than the gap length limit. The maximum gap length is a value larger than the maximum limit value (gap limit length) recognized as a gap length.

Examples of the above-mentioned parameter settings are shown in Figure 13. In Figure 13, (a) and (b) are examples of setting the PDU length limit and the number of PDU elements, where (a) shows an example where the PDU length limit is set to 1000 and the number of PDU elements is set to 5, and (b) shows an example where the PDU length limit is set to 1500 and the number of PDU elements is set to 4.

The settings shown in FIG. 13(a) allow PDUs with lengths of 1-200, 201-400, 401-600, 601-800, and 801-1000 to be selectively extracted, and all PDUs over 1000 can be treated as having the longest PDU length attribute. The settings shown in FIG. 13(b) allow PDUs with lengths of 1-375, 376-750, 751-1125, and 1126-1500 to be selectively extracted, and all PDUs over 1500 can be treated as having the longest PDU length attribute.

In addition, in FIG. 13, (c) and (d) are examples of setting the gap length limit, the number of gap elements, and the maximum gap length. (a) shows an example where the gap length limit is set to 1500, the number of gap elements is set to 3, and the maximum gap length is set to 10000, and (b) shows an example where the gap length limit is set to 1000, the number of gap elements is set to 7, and the maximum gap length is set to 5000.

The settings shown in FIG. 13(c) allow gaps with lengths of 1 to 1333, 1334 to 2666, 2667 to 4000, and 4001 to 10000 to be selectively extracted, and gaps longer than 10000 are not treated as having a gap length. The settings shown in FIG. 13(d) allow gaps with lengths of 1 to 142, 143 to 285, 286 to 428, 429 to 571, 572 to 714, 715 to 857, 858 to 1000, and 1001 to 5000 to be selectively extracted, and gaps longer than 5000 are not treated as having a gap length.

<Traffic Fluctuation Prediction Processing>
On the other hand, the traffic fluctuation prediction unit 45 serving as the above-mentioned traffic fluctuation prediction function performs processing for predicting traffic fluctuations based on the communication traffic pattern estimated by the traffic pattern estimation unit 44 .

In the traffic fluctuation prediction process, the traffic fluctuation prediction unit 45 records and analyzes the appearance of the communication traffic patterns estimated by the traffic pattern estimation unit 44 and the subsequent traffic fluctuations, and based on the results, derives the trends of individual patterns and traffic fluctuations (load fluctuations) and the probability of traffic fluctuations using probability and statistical methods, and predicts communication traffic fluctuations based on this.

The parameters required for the traffic fluctuation prediction process include the load fluctuation aggregation period. The load fluctuation aggregation period can be set to the short-term, medium-term, or long-term time length for aggregating the load fluctuation. When predicting traffic fluctuations, the traffic fluctuation prediction unit 45 performs the traffic fluctuation prediction process for a pre-set short-term, medium-term, or long-term period. Here, the short term may be set to a period of, for example, several seconds to 10-odd seconds, and the long term may be set to a period of, for example, several tens of seconds to several minutes. In this case, the medium term is a period length between several tens of seconds and several minutes.

Functional blocks for implementing the above-mentioned traffic pattern estimation process and OTA traffic fluctuation prediction process are shown in FIG. 11. In the functional blocks shown in FIG. 11, the database 33 is provided with a processed data storage unit 33a for storing processed data in the blind signal estimation device 20, as well as an element definition table 33b, a segment definition table 33c, a traffic pattern table 33d, and a monitoring pattern table 33e. The traffic pattern estimation unit 44 and the traffic fluctuation prediction unit 45 can appropriately access any of the processed data storage unit 33a, the element definition table 33b, the segment definition table 33c, the traffic pattern table 33d, and the monitoring pattern table 33e in the database 33.

In FIG. 11, the element definition table 33b, segment definition table 33c, traffic pattern table 33d, and monitoring pattern table 33e are provided, for example, in database 33, but this is not limiting and they may be provided in a part other than database 33, such as the ROM of the computer device described above or other dedicated storage unit.

<Element definition table>
The element definition table 33b is, for example, a data storage unit (table) that stores classifications of PDU lengths and gap lengths generated according to setting parameters. The element definition table 33b has, for example, a parameter setting area 51 and an element definition area 52, as shown in Fig. 14. The parameter setting area 51 stores the setting values of the segment length, the number of shifts (width), the number of PDU length class divisions, the PDU length limit value, the number of gap length class divisions, the gap length limit value, and the maximum gap length.

The PDU length class division number is the number of classifications of PDUs whose length is up to the PDU length limit value, and the gap length class division number is the number of classifications of gaps whose length is up to the gap length class limit value. This example shows setting example 1, setting example 2, ... for the setting values. In this case, the appearance of PDUs that match the setting values for all settings is checked. For setting example 1, the setting values for segment length, shift count, PDU length class division number, PDU length limit value, gap length class division number, gap length limit value, and maximum gap length are 5, 1, 5, 1000, 3, 4000, and 10000, respectively. For setting example 2, the setting values for the above parameters are 5, 5, 4, 1500, 7, 1000, and 5000, respectively.

In the element definition area 52, various pieces of information are stored: the number of class boundary values for PDU length, a list of boundary values for class division by PDU length, the number of class boundary values for gap length, and a list of boundary values for class division by gap length. The number of class boundary values for PDU length is the number of boundary values for class division. The list of boundary values for class division by PDU length is a list of boundary values for class division regarding PDU length, and the number of members in the list is determined according to the number of divisions. In this example, the length of the "PDU length limit value" is divided equally by the value of the "number of PDU length class divisions", but a method of directly setting the value of this boundary value list as a parameter may also be used.

The number of class boundary values for gap length is the number of boundary values for class division. The list of boundary values for class division by gap length is a list of boundary values for class division regarding gap length, and the number of members in the list is determined according to the number of divisions. In this example, the length of the "gap length limit value" is equally divided by the value of the "gap length class division number", but it is also possible to directly set the value of this boundary value list as a parameter.

In the element definition area 52, the number of class boundary values for PDU length is set to 5 in accordance with the above-mentioned setting example 1, and the list of boundary values for class division by PDU length is P1 = 200, P2 = 400, P3 = 600, P4 = 800, P5 = 1000 (see FIG. 13(a)). The number of class boundary values for gap length is set to 4, and the list of boundary values for class division by gap length is G1 = 1333, G2 = 2666, G3 = 4000, G4 = 10000 (see FIG. 13(c)).

Also, in accordance with setting example 2, the number of class boundary values for PDU length is set to 4, and the list of boundary values for class division by PDU length is P1 = 375, P2 = 750, P3 = 1125, P4 = 1500 (see FIG. 13(b)). The number of class boundary values for gap length is set to 8, and the list of boundary values for class division by gap length is G1 = 142, G2 = 285, G3 = 428, G4 = 571, G5 = 714, G6 = 857, G7 = 1000, G8 = 5000 (see FIG. 13(d)).

<Segment definition table>
The segment definition table 33c is a table of segment information extracted from the input PDU data, and has a configuration for holding information on each item of segment ID (a unique ID number in the segment definition table 33c), number of elements, element information list, and load fluctuation information, as shown in Fig. 15(a), for example. In the example of Fig. 15(a), setting examples 1 to 4 are shown for the segment ID, number of elements, element information list, and load fluctuation information, corresponding to segment IDs = SEG #1, SEG #2, SEG #3, and SEG #4, respectively. Examples 1, 2, 3, and 4 are set with PDU values and GAP (gap) values corresponding to the communication sequences shown as examples 1 to 4 in Fig. 15(b), respectively.

For example 1, the number of elements is set to 5 corresponding to segment ID = SEG#1, and the element information list includes "Type = PDU, Size = 80", "Type = GAP, Size = 125", "Type = PDU, Size = 80", "Type = GAP, Size = 125", and "Type = PDU, Size = 80". For examples 2, 3, and 4, the number of elements and element information list are set to the values shown in Figure 5 (a) corresponding to segment ID = SEG#2, 3, and 4, respectively.

The load fluctuation information indicates the cumulative and average values of the subsequent short-, medium-, and long-term load (start time, required time, amount of transmitted data, etc.) fluctuations, and is stored in correspondence with each element in the element information list of Examples 1 to 4. In the example of FIG. 15, for convenience, it is shown as a blank space, but the load fluctuation information corresponding to segment ID = SEG#1 in Example 1 stores, for example, information on the load fluctuation of each element constituting the communication sequence shown in Example 1 of FIG. 15(b). Similarly, the load fluctuation information corresponding to segment IDs = SEG#2, SEG#3, and SEG#4 in Examples 2, 3, and 4, respectively, stores information on the load fluctuation of each element constituting the communication sequence shown in Examples 2, 3, and 4 of FIG. 15(b).

<Traffic Pattern Table>
The traffic pattern table 33d is a table that generates and stores a time-series segment appearance list from the input PDU data, taking into consideration interval data between consecutive PDU data, and has a configuration in which start time information, required time, and transmission data amount are stored corresponding to the segment ID, as shown in FIG. 16. Here, the start time information is information indicating the start time of each segment (multiple consecutive elements) identified by the segment ID. The required time is the time required to transmit multiple consecutive elements that make up the segment. The required time may be a value that can be converted to the required time, for example, a value equivalent to the data amount obtained by adding the cap time to the transmission data amount. The transmission data amount refers to the total value of the PDU size in the element. Usually, the segment ID corresponds to the segment ID registered in the segment definition table 33b illustrated in FIG. 15(a).

<Monitoring pattern table>
The monitoring pattern table 33e is a table for registering the segment IDs and monitoring times that have appeared up to that point when predicting traffic fluctuations (see steps S92 to S95 in FIG. 18), and has the segment IDs and monitoring times as entries (setting items). The traffic fluctuation prediction unit 45 refers to the load fluctuation information in the segment definition table 33c for all segment IDs registered in the monitoring pattern table 33e, and derives the traffic estimates for the communication traffic pattern within a predetermined time. Here, the traffic fluctuation prediction unit 45 is configured to erase the segment ID when the registered monitoring time has ended.

An example of the operation of the element definition table 33b, segment definition table 33c, traffic pattern table 33d, and monitoring pattern table 33e having the above-mentioned configuration will be described with reference to FIG. 11.

In the functional block illustrated in FIG. 11, the traffic pattern estimation unit 44 sets the element definition table 33b based on parameters inputted through a setting operation, for example, from the input unit 34 (or the display unit 35) (see S101).

Next, the traffic pattern estimation unit 44 reads element information for one segment from the processing data storage unit 33, which stores data such as the PDU estimation results sequentially output from the PDU estimation unit 43 (see S102a), extracts segment information according to parameter values (set values) that have already been set, and performs a process of compiling the element information, which is the element of the extracted segment information, into an element information list and registering it in the segment definition table 33c (see S102b) every time new segment information is extracted (appears).

In line with this, the traffic pattern estimation unit 44 extracts a communication traffic pattern (consisting of items such as start time, required time, and transmitted data volume) from element information, which is an element of the segment information registered in the segment definition table 33c (see S103a), and executes a process of registering the extracted communication traffic pattern in the traffic pattern table 33d (see S103b).

In conjunction with the registration of the element information list in step S102b above, the segment definition table 33c also registers load fluctuation information (see FIG. 15(a)) that indicates the load fluctuation corresponding to the segment information constituted by the element information list, which corresponds to the element information list that is an element of the segment information extracted according to the parameter setting value.

In the functional block illustrated in FIG. 11, the traffic fluctuation prediction unit 45 registers the segment ID stored in the segment definition table 33c and one of the preset monitoring times (e.g., short term, medium term, long term) in the monitoring pattern table 33e (step S104a). Then, for all the registered segment IDs, the traffic fluctuation prediction unit 45 refers to the load fluctuation information registered in the segment definition table 33c, counts the data volume of the communication traffic pattern identified by the segment ID stored in the traffic pattern table 33d, calculates it as a traffic estimate within the above monitoring time, and further executes a process of predicting subsequent traffic fluctuations based on the traffic estimate (step S104b).

In this way, in the OTA traffic analysis device 1, the traffic pattern estimation unit 44 and the traffic fluctuation prediction unit 45 are capable of performing the above-mentioned traffic pattern estimation process and traffic fluctuation prediction process by referring to the element definition table 33b, segment definition table 33c, traffic pattern table 33d, and monitoring pattern table 33e having the above-mentioned configuration.

Next, the traffic pattern estimation processing operation of the OTA traffic analysis device 1 according to this embodiment will be described in detail with reference to the flowchart shown in FIG. 17.

The traffic pattern estimation process (S8) shown in FIG. 17 can be performed by the traffic pattern estimation unit 44, for example, in conjunction with the PDU estimation process in step 7 of FIG. 8, or the traffic pattern estimation process can be started at a predetermined timing after the PDU estimation process. In either case, while the PDU estimation process is being performed, processing data including the PDU estimation result in the blind signal estimation device 20 is stored in the processing data storage unit 33a (see FIG. 11) of the database 33, and the traffic pattern estimation unit 44 performs processing to estimate the communication traffic pattern based on the processing data stored in the processing data storage unit 33a.

In the OTA traffic analysis device 1 according to this embodiment, in order to perform the traffic pattern estimation process (step S8) shown in FIG. 17, it is necessary to set parameters related to OTA traffic analysis. Specifically, in the data processing device 30, the analysis condition setting unit 41 accepts a user operation (input operation) on the input unit 34 or the display unit 35, and sets the input parameters (step S81).

Next, the traffic pattern estimation unit 44 generates an element definition table 33b and sets the parameter values accepted in step S1 in the element definition table 33b (see FIG. 14) (step S82). This process enables the element definition table 33b to be set to a table of data contents such as those shown in FIG. 14.

Regarding steps S81 and S82 above, for example, when a traffic pattern estimation process is executed following a PDU estimation process during a series of OTA traffic analysis processes shown in FIG. 8, in step S1, in addition to the parameters for the PDU estimation process, parameters necessary for the traffic pattern estimation process may also be set, and the traffic pattern estimation unit 44 may read the parameters set in step S1, generate an element definition table 33b based on the read parameters, and set the values of the parameters.

After the setting of the element definition table 33b is completed, the traffic pattern estimation unit 44 reads the element information for one segment that has been set from the processing data storage unit 33a (see FIG. 11) (step S83).

The traffic pattern estimation unit 44 then performs a process of identifying each successive element of the element information being read from the processing data storage unit 33a according to the parameter setting values set in the element definition table 33b (step S84).

The traffic pattern estimation unit 44 then checks whether the segment information identified in step S84 is new, and if it is new segment information, registers it as a new entry in the segment definition table 33c (see FIG. 15(a)) in association with the segment ID (step S85). At this time, the traffic pattern estimation unit 44 also starts managing the load fluctuation information (e.g., occurrence frequency, data volume, etc.) of the segment information registered as a new entry using the segment definition table 33c (see FIGS. 15 and 17).

Next, the traffic pattern estimation unit 44 registers the communication traffic pattern of the segment information registered as a new entry in step S85 in the traffic pattern table 33d (see Figures 16 and 20) in association with the same segment ID as in the segment definition table 33c (step S86).

Furthermore, for segment information that has been registered in the segment definition table 33c at least once in step S85, the traffic pattern estimation unit 44 continuously monitors the load fluctuation information, such as the start time, required time, and transmission data volume, for the registered segment information, and executes a process of sequentially updating the load fluctuation information managed in association with the segment information in the segment definition table 33c (step S87).

As shown in FIG. 17, by repeatedly executing the processes of steps S83 to S87, the traffic pattern estimation unit 44 is able to detect the appearance of a segment having a desired arrangement of elements using the segment definition table 33c, and while managing the load fluctuation information of those segments, monitor the communication traffic pattern of the appeared segment using the traffic pattern table 33d.

On the other hand, the traffic fluctuation prediction unit 45 performs a process (see step S9 in FIG. 17) of estimating (predicting) the traffic fluctuation for each communication traffic pattern stored in the traffic pattern table 33d by the above-mentioned series of processes, from the communication traffic patterns that have appeared up to that point, while referring to the load fluctuation information managed in the segment definition table 33c corresponding to the segment ID of the traffic pattern.

Next, the traffic fluctuation prediction processing operation of the OTA traffic analysis device 1 according to this embodiment will be described in detail with reference to the flowchart shown in FIG. 18. This traffic fluctuation prediction processing is performed by the traffic fluctuation prediction unit 45 of the data processing device 30 in conjunction with the execution of the traffic pattern estimation processing shown in FIG. 17.

When the traffic fluctuation prediction process is started, the traffic fluctuation prediction unit 45 accesses the traffic pattern table 33d used in the traffic fluctuation prediction process and detects the communication traffic pattern information stored corresponding to the segment ID (step S91).

The traffic fluctuation prediction unit 45 then reads out all segment IDs from the information on the communication traffic pattern detected in step S91, and registers all of these segment IDs and the monitoring time preset by the user in the monitoring pattern table 33e (step S92). The monitoring time can be set by the user according to the desired period (short term, medium term, long term), and once set, a countdown begins in conjunction with the start of traffic fluctuation processing, and stops when the countdown expires. This allows the traffic fluctuation prediction unit 45 to continuously perform fluctuation prediction processing of communication traffic patterns corresponding to all segment IDs during the set monitoring time.

After all segment IDs and monitoring times have been registered in the monitoring pattern table 33e, the traffic fluctuation prediction unit 45 refers to the load fluctuation information in the segment definition table 33c for all registered segment IDs, and performs a process of tallying up the amount of transmitted data for each, taking into account the remaining monitoring time (remaining time) (step S93).

The traffic fluctuation prediction unit 45 then aggregates the amount of transmission data collected in step S93 and outputs or displays it as a traffic estimate for a specified time period (e.g., the desired period mentioned above) (step S94).

Furthermore, the traffic fluctuation prediction unit 45 performs a process of deleting the segment IDs whose monitoring time has expired from the monitoring pattern table 33e (step S95). When all segment IDs have been deleted from the monitoring pattern table 33e, the traffic fluctuation prediction unit 45 ends the series of traffic fluctuation prediction processes shown in FIG. 18.

During the above-mentioned traffic fluctuation prediction process, in step S93, the process of tallying up the amount of transmitted data by referring to the load fluctuation information in the segment definition table 33c is described. However, the OTA traffic analysis device 1 according to this embodiment is capable of estimating (predicting) traffic fluctuation patterns consisting of various items including the amount of transmitted data.

In addition, in the OTA traffic analysis device 1 according to this embodiment, the monitoring time can be selectively set to short-term, medium-term, or long-term (see step S92), so it is possible to predict traffic fluctuations for a period of time desired by the user.

In addition, in the OTA traffic analysis device 1 according to this embodiment, the tables used for the traffic pattern estimation process and the traffic fluctuation prediction process are not limited to the data contents of the element definition table 33b, segment definition table 33c, traffic pattern table 33d, and monitoring pattern table 33e described above, but other data contents or data storage media having various forms may be applied.

For example, FIG. 20 shows an example of parameter settings related to the communication traffic pattern estimation process when assuming the communication sequence shown in FIG. 19, and an example of the configuration of each element and segment, and FIG. 21 shows an example of the configuration of element definition table 33b1 and segment definition table 33c1 that are set and generated during the traffic pattern estimation process for the above communication sequence based on the above parameter settings.

In the communication sequence shown in FIG. 19, one segment is made up of three elements, and for convenience, both the PDU length and the gap are represented by the symbol E, which is a numbered version of the PDU length. The segments are also represented by the symbols S1, S2, S3, S4, S5, S6, and S7.

In FIG. 20, (a) is a table showing an example of the settings of the various parameters mentioned above related to the traffic pattern estimation process when the communication sequence shown in FIG. 19 is assumed. (b) is a table showing an example of the type and size of each element when the above communication sequence is assumed, and (c) is a table showing an example of the configuration of each segment (see S1 to S2 in FIG. 19).

In FIG. 21, (a) shows an example of an element definition table 33b1 in which parameter values in the range shown in the table of FIG. 20(a) are set. (b) shows an example of a segment definition table 33c1 generated based on the parameter settings of the element definition table 33b1 during the traffic pattern estimation process.

In the traffic pattern estimation process using the above element definition table 33b1 and segment definition table 33c1, the traffic pattern estimation unit 44 also generates, for example, a traffic pattern table 33d as shown in FIG. 16 (see step S86 in FIG. 17).

In this way, the OTA traffic analysis device 1 according to this embodiment can realize a traffic pattern estimation process targeting the communication traffic pattern desired by the user by preparing the various element definition tables 33b (33b1), segment definition tables 33c (33c1), and traffic pattern tables 33d described above.

As described above, the OTA traffic analysis device 1 according to the first embodiment intermittently captures a multi-wave mixed signal that is a mixture of wireless signals transmitted and received between the base station B1 and the wireless terminal A1 in an OTA environment in which wireless communication is performed between the base station B1 and multiple wireless terminals A1, and analyzes wireless communication traffic based on a separated signal that is separated for each signal source 110, which is the wireless terminal A1, from the received signal intermittently acquired in response to the capture.

The OTA traffic analysis device 1 according to the first embodiment includes a capture control unit 31a that controls the signal capture time and reception interval of a multi-wave mixed signal, a PDU estimation unit 43 that selects separated signals separated after capture for each component of the same communication, estimates the signal pattern of the selected separated signals for each component of the same communication, and estimates the PDU transmitted and received by wireless communication for each component of the same communication based on the estimated signal pattern of the separated signal, a traffic pattern estimation unit 44 that performs processing to estimate a communication traffic pattern defined by PDU data and interval data between PDU data based on the PDU estimation result by the PDU estimation unit 43, and a traffic fluctuation prediction unit 45 that performs processing to estimate the data volume of the communication traffic pattern within a predetermined period based on the communication traffic pattern estimation result by the traffic pattern estimation unit 44, and predicts the subsequent communication traffic load fluctuation of the communication traffic pattern from the estimated data volume.

With this configuration, the OTA traffic analysis device 1 according to the first embodiment can estimate PDUs from radio signals received in the OTA space, and efficiently estimate communication traffic patterns in OTA communications from the PDU estimation results, as well as predict subsequent communication traffic load fluctuations.

The OTA traffic analysis device 1 according to the first embodiment further includes an analysis condition setting unit 41 that sets parameters related to the analysis of communication traffic. The analysis condition setting unit 41 sets the values of each item of a segment, which is a plurality of consecutive elements, including a PDU length, which is the length of a PDU, a gap, which is the interval between two consecutive PDUs, and the PDU length and gap are also called elements, as parameters related to the process of estimating a communication traffic pattern. The traffic pattern estimation unit 44 is configured to select the sequence of consecutive PDUs estimated by the PDU estimation unit 43 in an array in which each item satisfies the respective set value, and extract it as a communication traffic pattern.

With this configuration, the OTA traffic analysis device 1 according to the first embodiment can easily and efficiently extract a communication traffic pattern of an array having values of PDU length, gap, element, and segment that correspond to the set values of each of the items, based on the PDU estimation results, by setting parameters for each of the items, namely, PDU length, gap, element, and segment.

In addition, in the OTA traffic analysis device 1 according to the first embodiment, the analysis condition setting unit 41 further sets a shift width, which is an item indicating the number of elements to be moved to the next segment, as a parameter related to the process of estimating the communication traffic pattern, and the traffic pattern estimation unit 44 is configured to extract the sequence of PDUs estimated by the PDU estimation unit 43 multiple times by shifting the time equivalent to the shift width.

With this configuration, the OTA traffic analysis device 1 according to the first embodiment can improve the estimation accuracy by narrowing the shift width, but at the cost of a larger processing load, while widening the shift width can reduce the processing load, but at the cost of a worsening estimation accuracy. This makes it possible to selectively set a desirable shift width in terms of both estimation accuracy and processing load, and to carry out the estimation process of the communication traffic pattern.

In the OTA traffic analysis device 1 according to the first embodiment, the analysis condition setting unit 41 is configured to set values for each of the following parameters related to the process of estimating the communication traffic pattern: segment length, which is the number of elements in one of the segments; PDU length limit, which is information indicating that all PDUs longer than this are classified as the longest PDU length attribute; PDU element number, which is information specifying how many divisions the element length is to be classified into for PDUs that do not exceed the PDU length limit; gap length limit, which is information indicating that all gaps longer than this and that do not exceed the maximum gap length are classified as the longest gap length attribute; gap element number, which is information specifying how many divisions the element length is to be classified into for gaps that do not exceed the gap length limit; and maximum gap length, which is the maximum limit value recognized as a gap length.

With this configuration, the OTA traffic analysis device 1 according to the first embodiment can estimate communication traffic patterns based on further detailed parameter settings including the items of segment length, PDU length limit, number of PDU elements, gap length limit, number of gap elements, and maximum gap length, which is expected to improve the accuracy of estimating communication traffic patterns.

In addition, in the OTA traffic analysis device 1 according to the first embodiment, the analysis condition setting unit 41 sets a monitoring period for aggregating the load fluctuation of the communication traffic pattern as a parameter related to the process of estimating the communication traffic pattern, and the traffic fluctuation prediction unit 45 is configured to estimate the amount of data during the set monitoring period.

With this configuration, the OTA traffic analysis device 1 according to the first embodiment can aggregate the data volume during the monitoring period set by the user for the communication traffic pattern estimated by the traffic pattern estimation unit 44, and can predict traffic fluctuations based on user-driven settings.

In addition, in the OTA traffic analysis device 1 according to the first embodiment, the analysis condition setting unit 41 selectively sets the monitoring period from short-term, medium-term, to long-term, and the traffic fluctuation prediction unit 45 is configured to estimate the amount of data during any of the set short-term, medium-term, or long-term monitoring periods.

With this configuration, the OTA traffic analysis device 1 according to the first embodiment can estimate the communication traffic load fluctuation for the short-term, medium-term, or long-term monitoring period desired by the user for the communication traffic pattern estimated by the traffic pattern estimation unit 44, and can smoothly predict subsequent traffic fluctuations according to the length of each monitoring period.

In addition, the OTA traffic analysis method according to the first embodiment is for analyzing wireless communication traffic based on separated signals separated for each signal source 110, which is a wireless terminal A1, from a received signal obtained by intermittently capturing a multi-wave mixed signal in which wireless signals transmitted and received between a base station B1 and a wireless terminal A1 are mixed in an OTA environment in which wireless communication is performed between a base station B1 and a plurality of wireless terminals A1. The method includes steps of: selecting the separated separated signals after capture for each component of the same communication; estimating the signal pattern of the selected separated signals for each component of the same communication; and, selecting the separated signals for each component of the same communication based on the signal pattern of the estimated separated signals (S61 to S66). The method includes a PDU estimation step (S7) for estimating the PDU transmitted and received by wireless communication for each component of the communication, a traffic pattern estimation step (S8) for estimating a communication traffic pattern defined by PDU data and interval data between PDU data based on the PDU estimation result from the PDU estimation step, and a traffic fluctuation prediction step (S9) for estimating the amount of data within a predetermined period of the communication traffic pattern based on the communication traffic pattern estimation result from the traffic pattern estimation step, and predicting the subsequent communication traffic load fluctuation of the communication traffic pattern from the estimated data amount.

With this configuration, by applying the OTA traffic analysis method according to the first embodiment to the OTA traffic analysis device 1 having the above-described configuration, it is possible to estimate a PDU from a received radio signal in the OTA space, and efficiently estimate the communication traffic pattern in OTA communication from the PDU estimation result, and also to predict the subsequent load fluctuation of the communication traffic.

Second Embodiment
The configuration of the OTA traffic analysis device 1A according to the second embodiment is shown in Fig. 22. As shown in Fig. 22, the OTA traffic analysis device 1A according to the second embodiment includes a PDU estimation unit 43A instead of the PDU estimation unit 43 in the data processing device 30 of the OTA traffic analysis device 1 according to the first embodiment. The PDU estimation unit 43 separates signals contained in an intermittently received received signal for each component of the same communication (signal source 110) and estimates the length of the PDU based on the arrival direction and signal strength of the separated signals, whereas the PDU estimation unit 43A directly inputs the separated signals separated from the received signal by the signal separation unit 23 without using the arrival direction and signal strength, and estimates the length of the PDU based on the separated signal pattern.

The OTA traffic analysis device 1A according to the second embodiment has the same configuration as the OTA traffic analysis device 1 according to the first embodiment, except for the PDU estimation unit 43A. In FIG. 22, the same reference numerals are used to designate the same components as those of the OTA traffic analysis device 1 according to the first embodiment.

The PDU estimation process of the OTA traffic analysis device 1A according to the second embodiment will be described with reference to the flowchart shown in FIG. 23. In FIG. 23, the same processes as those in the PDU estimation process of the OTA traffic analysis device 1 according to the first embodiment shown in FIG. 8 are given the same step numbers, and detailed descriptions of the same processes will be omitted. Here, the process unique to the OTA traffic analysis device 1A according to the second embodiment will be mainly described.

In the OTA traffic analysis device 1A according to the second embodiment, the PDU estimation unit 43A first performs an analysis condition setting process (step S1), similar to the OTA traffic analysis device 1 according to the first embodiment.

Then, similarly to the OTA traffic analysis device 1 according to the first embodiment, the PDU estimation unit 43A receives an analysis start operation (step S2), drives the blind signal estimation device 20 to intermittently capture a multi-wave mixed signal in which multiple wireless signals are mixed (step S3), and separates signal components for each signal source 110 from the captured signal by the signal separation unit 23 of the blind signal estimation device 20 (step S4).

The signal separator 23 inputs the multiple separated signal source components (separated signals separated for each signal source 110) to the PDU estimator 43A of the data controller 31b. Here, the PDU estimator 43A takes in the separated signals input from the signal separator 23 and performs a process of continuously storing the separated signals in a specified storage area of the database 33 (step S6A).

The signal processing up to step S6A will be described with reference to FIG. 24. In the OTA traffic analysis device 1A according to the second embodiment, as shown in FIG. 24, a multi-wave mixed signal, which is a mixture of multiple radio signals transmitted and received between a base station B1 and a wireless terminal A1 in communication, is intermittently received by the blind signal estimation device 20 (see FIG. 24(a)).

In the OTA traffic analysis device 1A, the intermittently received multi-wave mixed signal is subjected to signal processing by the frequency conversion unit 21 and the AD conversion unit 22 of the blind signal estimation device 20 (see FIG. 24(b)), and then separated for each signal source 110 by the signal separation unit 23, and recorded in the database 33 as a separated signal (see FIG. 24(c)). In FIG. 24(c), the separated signals shown with the same shading each indicate components of the same communication. Here, four types of shading are used to show the arrangement of separated signals S11, S12, S13, and S14 relating to four types of data communication. The arrangement of the separated signals S11, S12, S13, and S14 here constitutes an intermittent separated signal pattern of the PDUs transmitted and received by the four types of data communication. This allows the PDU estimation unit 43A, like the PDU estimation unit 43 of the OTA traffic analysis device 1, to estimate (obtain an estimated PDU) the PDUs corresponding to each of the data A1, B1, C1, and D1 for each type of communication data based on the above separation signal pattern.

That is, during the series of processes shown in FIG. 23, the PDU estimation unit 43A searches for the separation signal pattern of the stored separation signal in conjunction with the process of continuously recording the separation signal in step S6A, and performs a process of estimating the size of the PDU that includes the separation signal as a partial component based on the separation signal pattern (step S7A).

The timing chart of the signal processing of step S7A is shown in Figures 24(d) and (e). In the OTA traffic analysis device 1A according to the second embodiment, as shown in Figure 24(d), a process is performed to estimate the PDUs corresponding to data A1, B1, C1, and D1, respectively, from the separated signal patterns of separated signals S11, S12, S13, and S14 (see Figure 24(c)) generated in the previous process.

This PDU estimation process is performed by the PDU estimation unit 43A by applying the decision flow shown in Figure 9 in light of the relationship between the received signal, separated signal, and estimated PDU shown in Figure 10. According to this PDU estimation process, as shown in Figure 24(e), it is possible to estimate the PDUs corresponding to data A1, B1, C1, and D1 in the sections where separated signals S11, S12, S13, and S14 shown in Figure 24(c) are continuous.

As described above, the OTA traffic analysis device 1A according to the second embodiment has a device control unit 31a that controls the signal capture time and reception interval of a multi-wave mixed signal, and a PDU estimation unit 43A that selects separated signals separated after capture for each component of the same communication, estimates the signal pattern of the selected separated signals for each component of the same communication, and estimates the PDU transmitted and received by wireless communication for each component of the same communication based on the estimated signal pattern of the separated signal.

Here, unlike the PDU estimation unit 43 according to the first embodiment, the PDU estimation unit 43A has a configuration that estimates a separated signal pattern without referring to the arrival direction and signal strength of the separated signal, and further estimates a PDU from the separated signal pattern. With this configuration, the OTA traffic analysis device 1A according to the second embodiment does not need to perform the process of estimating the arrival direction of the separated signal by the arrival direction estimation processing unit 24 of the blind signal estimation device 20, and the process of estimating the signal strength of the separated signal by the signal analysis unit 25, and it is possible to reduce the processing load of the PDU estimation unit 43 and to alleviate the demand for increased processing power.

Furthermore, the OTA traffic analysis device 1A according to the second embodiment has, in addition to the above-mentioned PDU estimation unit 43A, a traffic pattern estimation unit 44 and a traffic fluctuation prediction unit 45 equivalent to those of the OTA traffic analysis device 1 according to the first embodiment. The traffic pattern estimation unit 44 performs a traffic pattern estimation process according to the procedure shown in FIG. 17, and the traffic fluctuation prediction unit 45 performs a traffic fluctuation prediction process according to the procedure shown in FIG. 18. As a result, the OTA traffic analysis device 1A according to the second embodiment can be expected to achieve the same effects as the OTA traffic analysis device 1 according to the first embodiment in terms of the traffic pattern estimation process and traffic fluctuation prediction process.

As described above, the present invention has the effect of being able to efficiently estimate the traffic pattern of OTA communications from the received wireless signal in the OTA space, and also being able to predict its fluctuations, and is useful for OTA traffic analysis devices and OTA traffic analysis methods in general that are required to intermittently receive a multi-wave mixed signal.

REFERENCE SIGNS LIST 1, 1A OTA traffic analysis device 6 Observation area 10 Antenna device 20 Blind signal estimation device 20a Receiving unit 20b Analysis processing unit 21 Frequency conversion unit 22 AD conversion unit 23 Signal separation unit 24 Arrival direction estimation processing unit (arrival direction estimation means)
25 Signal analysis unit (signal strength estimation means)
30 Data processing device 31 Control unit 31a Device control unit (capture control means)
31b Data control unit 33 Database 33a Processing data storage unit 33b, 33b1 Element definition table 33c, 33c1 Segment definition table 33d Traffic pattern table 33e Monitoring pattern table 34 Input unit (setting means)
35 Display section 41 Analysis condition setting section (setting means)
42 Antenna control unit 43, 43A PDU estimation unit (PDU estimation means)
44 Traffic pattern estimation unit (traffic pattern estimation means)
45 Traffic fluctuation prediction unit (traffic fluctuation prediction means)
47 Display control unit 110 Signal source A1 Wireless terminal

Claims (7)

In an OTA environment in which wireless communication is performed between a base station (B1) and a plurality of wireless terminals (A1), an OTA traffic analysis device (1, 1A) is provided that analyzes communication traffic of the wireless communication based on a separated signal separated for each of the signal sources (110) that are the wireless terminals from a received signal obtained by intermittently capturing a multi-wave mixed signal in which wireless signals transmitted and received between the base station and the wireless terminals are mixed,
A capture control means (31a) for controlling a signal capture time and a reception interval of the multi-wave mixed signal;
a PDU estimation means (43, 43A) for selecting the separated signals separated after capture for each component of the same communication, estimating a signal pattern of the selected separated signals for each component of the same communication, and estimating a protocol data unit (PDU) transmitted/received by the wireless communication for each component of the same communication based on the estimated signal pattern of the separated signals;
a traffic pattern estimation means (44) for performing a process of estimating a communication traffic pattern defined by PDU data and interval data between the PDU data based on a result of the estimation of the protocol data unit by the PDU estimation means;
a traffic fluctuation prediction means (45) for estimating a data volume of the communication traffic pattern within a predetermined period based on a result of the estimation of the communication traffic pattern by the traffic pattern estimation means, and for predicting a subsequent communication traffic load fluctuation of the communication traffic pattern from the estimated data volume;
An OTA traffic analysis device comprising: The method further comprises setting means (41) for setting parameters related to the analysis of the communication traffic,
the setting means sets values of each item of a PDU length, which is a length of the PDU, a gap, which is an interval between two consecutive PDUs, the PDU length and the gap, which are also called elements, and a segment, which is a plurality of consecutive elements, as the parameters related to the process of estimating the communication traffic pattern;
The OTA traffic analysis device according to claim 1, characterized in that the traffic pattern estimation means selects a sequence of consecutive PDUs estimated by the PDU estimation means in an array in which each item satisfies its respective set value, and extracts it as the communication traffic pattern. the setting means further sets a shift width, which is an item indicating the number of elements to be moved to a next segment, as the parameter related to the process of estimating the communication traffic pattern;
3. The OTA traffic analysis device according to claim 2, wherein the traffic pattern estimation means extracts the sequence of the PDUs estimated by the PDU estimation means a plurality of times with a time shift corresponding to the shift width. 3. The OTA traffic analysis device according to claim 2, wherein the setting means sets values of the following items as the parameters related to the process of estimating the communication traffic pattern: a segment length which is the number of elements in one of the segments; a PDU length limit which is information indicating that all of the PDUs longer than the segment length are classified into the longest PDU length attribute; a number of PDU elements which is information specifying how many divisions the PDUs are to be classified into for the PDUs that do not exceed the PDU length limit; a gap length limit which is information indicating that all of the gaps longer than the segment length and that do not exceed a maximum gap length are to be classified into the longest gap length attribute; a number of gap elements which is information specifying how many divisions the gaps are to be classified into for the gaps that do not exceed the gap length limit; and a maximum gap length which is a maximum limit value recognized as a gap length. the setting means sets a monitoring period for counting load fluctuations of the communication traffic pattern as the parameter related to the process of estimating the communication traffic pattern;
3. The OTA traffic analysis device according to claim 2, wherein the traffic fluctuation prediction means estimates the amount of data during the set monitoring period. The setting means selectively sets a monitoring period from a short term, a medium term, and a long term in that order,
6. The OTA traffic analysis device according to claim 5, wherein the traffic fluctuation prediction means estimates the data volume during any one of the short-term, medium-term, and long-term monitoring periods that have been set. In an OTA environment in which wireless communication is performed between a base station (B1) and a plurality of wireless terminals (A1), an OTA traffic analysis method is provided for analyzing communication traffic of the wireless communication based on a separated signal separated for each signal source (110) that is the wireless terminal from a received signal obtained by intermittently capturing a multi-wave mixed signal in which wireless signals transmitted and received between the base station and the wireless terminals are mixed, the method comprising:
selecting the separated signals separated after the capture for each component of the same communication, and estimating a signal pattern of the selected separated signals for each component of the same communication (S61 to S66);
a PDU estimation step (S7) of estimating a protocol data unit (PDU) transmitted/received by the wireless communication for each component of the same communication based on the estimated signal pattern of the separated signal;
a traffic pattern estimation step (S8) of estimating a communication traffic pattern defined by PDU data and interval data between the PDU data based on the estimation result of the protocol data unit by the PDU estimation step;
a traffic fluctuation prediction step (S9) of estimating a data amount of the communication traffic pattern within a predetermined period based on a result of the estimation of the communication traffic pattern by the traffic pattern estimation step, and predicting a subsequent communication traffic load fluctuation of the communication traffic pattern from the estimated data amount;
13. A method for analyzing OTA traffic, comprising:

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