Radio quality estimation system, radio quality estimation device, radio quality estimation method, and program

Abstract

In order to make it possible to estimate the radio quality of a wireless terminal that changes depending on the usage status of a network by using a layer 1 parameter of the wireless terminal that can be passively acquired regardless of a communication status, this radio quality estimation system comprises: an acquisition unit that acquires the layer 1 parameter of the wireless terminal; a parameter estimation unit that estimates a first layer 2 parameter that does not depend on the usage status of the network on the basis of the layer 1 parameter; and a radio quality estimation unit that estimates the radio quality of the wireless terminal on the basis of past statistical values of the layer 1 parameter, the first layer 2 parameter, and a second layer 2 parameter that depends on the usage status of the network.

Description

Wireless quality estimation system, wireless quality estimation device, wireless quality estimation method, and program

[0001] The present invention relates to a wireless quality estimation system, a wireless quality estimation device, a wireless quality estimation method, and a program.

[0002] There are wireless quality prediction techniques for predicting wireless quality in wireless communication systems. For example, a technique is known that predicts the wireless quality of a wireless terminal after a predetermined time has elapsed by using location information of the wireless terminal, wireless environment information, etc., and a wireless quality prediction engine that has been trained by machine learning in advance.

[0003] Keisuke Wakao, Kenichi Kawamura, Takatsugu Moriyama, "Quality Prediction Technology for Optimal Use of Multiple Wireless Accesses," NTT Technical Journal, April 2020, pp. 11-13. 3GPP TS 36.214 V17.0.0 New Generation Mobile Communications System Committee Technical Review Working Group Study Status, Internet<URL: https: / / www.soumu.go.jp / main_content / 000538001.pdf>

[0004] In conventional techniques, throughput is predicted based on actual values ​​obtained in advance, but it is difficult to predict because degradation of wireless quality occurs differently depending on time and location. For example, parameters such as the number of resource blocks (RBs) allocated and the modulation and channel coding scheme (MCS), which affect wireless quality such as throughput, vary significantly depending on the usage status of the wireless communication network.

[0005] Therefore, in order to predict wireless quality based on previously acquired performance values, it is necessary to match the network usage status of the application used by the wireless terminal that acquires the performance values, and the network usage status of other wireless terminals, etc., to the situation to be predicted. However, setting such a situation is extremely difficult.

[0006] An embodiment of the present invention has been made in consideration of the above-mentioned problems, and makes it possible to estimate the wireless quality of a wireless terminal, which changes depending on the network usage status, using Layer 1 parameters of the wireless terminal that can be passively obtained regardless of the communication status.

[0007] In order to solve the above-mentioned problems, a wireless quality estimation system according to an embodiment of the present invention includes an acquisition unit that acquires Layer 1 parameters of a wireless terminal, a parameter estimation unit that estimates a first Layer 2 parameter that is independent of network usage status based on the Layer 1 parameters, and a wireless quality estimation unit that estimates wireless quality of the wireless terminal based on past statistics of the Layer 1 parameters, the first Layer 2 parameter, and a second Layer 2 parameter that is dependent on network usage status.

[0008] According to an embodiment of the present invention, it becomes possible to estimate the wireless quality of a wireless terminal, which changes depending on the network usage status, by using layer 1 parameters of the wireless terminal that can be passively acquired regardless of the communication status.

[0009] FIG. 1 is a diagram (1) showing an example of the configuration of a wireless quality estimation system according to the present embodiment. FIG. 2 is a diagram for explaining a layer 1 parameter according to the present embodiment. FIG. 3 is a diagram for explaining a first layer 2 parameter according to the present embodiment. FIG. 4 is a diagram for explaining past actual values ​​of a second layer 2 parameter according to the present embodiment. FIG. 5 is a diagram for explaining estimation of wireless quality according to Example 2. FIG. 6 is a diagram (2) showing an example of the configuration of a wireless quality estimation system according to the present embodiment. FIG. 7 is a flowchart showing an example of wireless quality estimation processing according to the present embodiment. FIG. 8 is a diagram for explaining an example according to the present embodiment. FIG. 9 is a diagram (1) showing an example of an estimation result of wireless quality according to the present embodiment. FIG. 10 is a diagram (2) showing an example of an estimation result of an example according to the present embodiment. FIG. 11 is a diagram showing an example of the hardware configuration of a computer. FIG. 2 is a diagram showing an example of a conventional wireless quality prediction technology.

[0010] Hereinafter, an embodiment of the present invention will be described with reference to the drawings. The embodiment described below is merely an example, and the embodiment to which the present invention is applied is not limited to the following embodiment.

[0011] <Wireless Quality Estimation System> A wireless quality estimation system is a system that estimates the communication quality of wireless communication (hereinafter referred to as wireless quality) in a wireless communication network. Here, before describing the wireless quality estimation system according to this embodiment, an overview of conventional wireless quality estimation techniques will be described.

[0012] 12 shows an overview of a wireless quality prediction system 1 disclosed in Non-Patent Document 1. A wireless quality prediction engine 10 is, for example, a server device on a network installed on a cloud or within a management network.

[0013] The terminal 2 periodically uploads information collected by the terminal 2, such as location information, terminal type, wireless access type, scan data of the wireless environment, and communication quality information, to the wireless quality prediction engine 10, and accumulates history information in a database 11. Based on this database 11, the wireless quality prediction engine 10 performs machine learning processing of a learning algorithm 12.

[0014] A terminal 2 using a wireless communication network makes an inquiry about wireless quality to the wireless quality prediction engine 10, adding, for example, wireless environment information, location information, etc. The wireless quality prediction engine 10 inputs the wireless environment information, location information, etc. included in the wireless quality inquiry into a learned learning algorithm 12, thereby predicting the wireless quality (e.g., throughput, delay, jitter, packet loss, etc.) of the terminal 2 and notifying the terminal 2. This allows the terminal 2 to obtain a future predicted value (predicted quality m minutes from now) of the wireless quality that will be obtained when the terminal 2 connects to an estimation-target wireless base station 13 that is visible in the vicinity.

[0015] In the technology disclosed in Non-Patent Document 1, a wireless quality prediction engine 10 predicts throughput based on actual values ​​acquired in advance. However, wireless quality such as throughput is difficult to predict because it changes significantly depending on the usage status of the wireless communication network. For example, in a public cellular network, the number of RBs (Resource Blocks) allocated, which has a significant impact on throughput, is controlled by the wireless communication system according to the communication status. Furthermore, communication parameters such as MCS (Modulation and Channel Coding Scheme) change depending on the traffic volume of applications used by terminals, which affects throughput.

[0016] Therefore, when predicting wireless quality based on previously acquired performance values, it is necessary to adjust the network usage status of the applications used by the wireless terminal that acquires the performance values, and the network usage status of other wireless terminals, etc., to the situation to be predicted. However, setting such a situation is extremely difficult.

[0017] Therefore, the wireless quality estimation system according to this embodiment has a system configuration as shown in FIG. 1, as an example, so that the wireless quality of a wireless terminal can be estimated using layer 1 parameters that are not affected by the network usage status.

[0018] <System Configuration> Fig. 1 is a diagram (1) showing an example of the configuration of a wireless quality estimation system according to this embodiment. In the example of Fig. 1, a wireless quality estimation system 100 includes a wireless terminal 110 and a wireless base station 120 that performs wireless communication with the wireless terminal 110. The wireless base station 120 is an example of a wireless quality estimation device according to this embodiment.

[0019] (Functional Configuration of Wireless Terminal) The wireless terminal 110 has a computer configuration, and by executing a predetermined program on the computer, realizes, for example, each functional configuration as shown in Fig. 1. In the example of Fig. 1, the wireless terminal 110 has each functional configuration such as an information collection unit 111, an information transmission unit 112, an information reception unit 113, and a storage unit 114. Note that at least a part of each of the above functional configurations may be realized by hardware.

[0020] The information collection unit 111 executes a collection process to collect layer 1 parameters of the wireless terminal 110 that can be passively acquired regardless of the communication situation.

[0021] 2 is a diagram for explaining layer 1 parameters according to this embodiment. The information collection unit 111 acquires layer 1 parameters that can be easily acquired without imposing a communication load on the wireless terminal 110 moving within the wireless communication area 200. Here, the layer 1 parameters may include, for example, RSRP (Reference Signal Received Power), RSSI (Received Signal Strength Indicator), RSRQ (Reference Signal Received Quality), and SINR (Signal-to-Interference-plus-Noise Ratio).

[0022] Here, RSRP is an index indicating the received power of a reference signal measured by the wireless terminal 110. RSSI is an index indicating the received power of the entire band measured by the wireless terminal 110. RSRQ is an index indicating the quality of a received reference signal, calculated from, for example, RSSI and RSRP. In this way, an index calculated from other layer 1 parameters collected by the information collection unit 111 may be included. SINR is an index indicating the ratio of the power of a desired signal to the power of components other than the desired signal (interference waves from other cells, thermal noise, etc.) in the received signal. These indices are used as KPIs (Key Performance Indicators) for evaluating the physical layer (Layer 1) of a wireless communication network.

[0023] The information transmitting unit 112 executes information transmitting processing to transmit the layer 1 parameters collected by the information collecting unit 111 to the radio base station 120. The information receiving unit 113 executes information receiving processing to receive an estimated value of radio quality notified from the radio base station 120. The storage unit 114 stores, for example, the layer 1 parameters collected by the information collecting unit 111 and / or the estimated value of radio quality received by the information receiving unit 113.

[0024] (Functional Configuration of Radio Base Station) The radio base station 120 has a computer configuration, and by executing a predetermined program on the computer, realizes, for example, each functional configuration as shown in Fig. 1. In the example of Fig. 1, the radio base station 120 has each functional configuration such as an acquisition unit 121, a parameter estimation unit 122, a radio quality estimation unit 123, a notification unit 124, and a storage unit 125. Note that at least a part of each of the above functional configurations may be realized by hardware.

[0025] The acquisition unit 121 executes an acquisition process to acquire Layer 1 parameters of the wireless terminal 110 that can be passively acquired regardless of the communication state. For example, the acquisition unit 121 receives Layer 1 parameters transmitted by the information transmission unit 112 of the wireless terminal 110.

[0026] The parameter estimation unit 122 executes a parameter estimation process to estimate layer 2 parameters (hereinafter referred to as first layer 2 parameters) that do not depend on the usage status of the wireless communication network, based on the layer 1 parameters acquired by the acquisition unit 121. Here, the first layer 2 parameters may include information such as a channel quality indicator (CQI), a modulation coding scheme (MCS), a block error rate (BLER), and a precoding matrix indicator (PMI).

[0027] The CQI is reception quality information indicating the downlink propagation path conditions measured by the wireless terminal 110. The MCS is information indicating a combination of a modulation scheme and a coding rate. As the MCS value increases, higher throughput is obtained, but higher reception power is required. The BLER is information indicating the block error rate measured by the wireless terminal 110. The PMI is an index fed back from the wireless terminal 110 to specify a suitable downlink precoder.

[0028] For example, the parameter estimation unit 122 calculates the first layer 2 parameter from the layer 1 parameter acquired by the acquisition unit using a theoretical formula. This theoretical formula may be a known theoretical formula or a calculation formula uniquely determined by a vendor such as a telecommunications carrier.

[0029] As another example, the parameter estimation unit 122 may estimate the layer 2 parameters from the layer 1 parameters using a regression equation or a machine learning model that has previously learned the relationship between the layer 1 parameters and the layer 2 parameters.

[0030] For example, as shown in Fig. 3, a wireless terminal 301 is set in the same communication environment as the prediction target (using the same application, for example), and a layer 1 parameter and a layer 2 parameter are acquired through advance measurement. Furthermore, a machine learning model 302 (e.g., a deep neural network) is trained in advance using the acquired layer 1 parameter and layer 2 parameter as training data to estimate the layer 2 parameter from the layer 1 parameter. The parameter estimation unit 122 may estimate the first layer 2 parameter by inputting the layer 1 parameter acquired by the acquisition unit 121 into the pre-trained machine learning model 302.

[0031] The wireless quality estimation unit 123 executes a wireless quality estimation process to estimate the wireless quality of the wireless terminal 110 based on the layer 1 parameters, the first layer 2 parameters, and layer 2 parameters that depend on the network usage status (hereinafter referred to as second layer 2 parameters). Here, the second layer 2 parameters may include information such as the number of resource blocks (RBs), a physical cell ID (PCI), an E-UTRAN absolute radio frequency channel number (EARFCN), and a bandwidth (BW).

[0032] RB is a unit of radio resource allocation. PCI is identification information for identifying a physical cell. EARFCN is a channel number indicating a radio frequency. BW etc. is information indicating a bandwidth.

[0033] For example, as shown in Fig. 4, the second layer 2 parameters are acquired by advance measurement using a wireless terminal 401 set in the same communication environment as the prediction target (using the same application, etc.). Furthermore, past statistical values ​​402 of the acquired second layer 2 parameters are calculated in advance and stored in the storage unit 125 of the wireless base station 120, etc.

[0034] In the example of FIG. 4, the past statistics 402 of the second layer 2 parameter indicate the cumulative distribution function (CDF) of the number of RBs (resource blocks).

[0035] As an example, the wireless quality estimator 123 calculates communication quality such as throughput from statistical values ​​of the layer 1 parameter, the first layer 2 parameter, and the second layer 2 parameter using a theoretical formula.

[0036] A known theoretical formula for calculating communication quality such as throughput based on communication parameters is, for example, formula (1) disclosed in Patent Document 3. However, the present invention is not limited to this formula, and the wireless quality estimation unit 123 may calculate communication quality using a theoretical formula independently determined by a vendor such as a telecommunications carrier.

[0037] Here, N ＭＩＭＯ is the maximum number of MIMO (Multi Input Multi Output) layers. ＭＯＤ is the number of bits per modulation symbol. f is a scaling factor for calculating the peak rate in the baseband processing of the wireless terminal. R ｍａｘ is the maximum coding rate. ＲＢ is the number of resource blocks per component carrier. ｓｙｍｂｏｌ is the time length [sec] per OFDM symbol. ＯＨ is the overhead rate per radio frame (reference signal, control channel, etc.). ＵＬ／ＤＬ is the allocation ratio of UL (uplink) and DL (downlink) in TDD (Time Division Duplex).

[0038] 5 , the wireless quality estimator 123 may use a machine learning model 501 that has been trained in advance to output a wireless quality estimation result using a layer 1 parameter, a first layer 2 parameter, and a second layer 2 parameter as input data. In this case, the wireless quality estimator 123 can obtain an estimation result of the wireless quality of the wireless terminal 110 by inputting the layer 1 parameter, the first layer 2 parameter, and the second layer 2 parameter into the trained machine learning model 501.

[0039] The notification unit 124 executes a notification process of notifying the wireless terminal 110 of the communication quality (for example, throughput) of the wireless terminal 110 estimated by the wireless quality estimation unit 123 .

[0040] The memory unit 125 stores various data, information, programs, etc., including, for example, the machine learning model 302 described in Figure 3, the statistical value 402 of the second layer 2 parameter described in Figure 4, and the machine learning model 501 described in Figure 5.

[0041] 1 is an example of the system configuration of the wireless quality estimation system 100. For example, as shown in FIG. 6 , the wireless quality estimation system 100 may include a calculation server 620 that can communicate with a wireless base station 610 and that includes an acquisition unit 121, a parameter estimation unit 122, a wireless quality estimation unit 123, a notification unit 124, a storage unit 125, etc.

[0042] The calculation server 620 is an information processing device having a computer configuration or a system including multiple computers. The calculation server 620 may be a server device installed on a cloud or in a management network, or may be a specific wireless base station.

[0043] The calculation server 620 executes a predetermined program on a computer provided in the calculation server 620, thereby realizing the acquisition unit 121, the parameter estimation unit 122, the wireless quality estimation unit 123, the notification unit 124, the memory unit 125, etc. described in Figures 1 to 5.

[0044] In this case, the radio base station 610 realizes a parameter receiving unit 601, a parameter transmitting unit 602, an estimation result receiving unit 603, an estimation result transmitting unit 604, etc. by executing a predetermined program on a computer provided in the radio base station 610.

[0045] The parameter receiving unit 601 executes a parameter receiving process to receive layer 1 parameters transmitted by the wireless terminal 110. The parameter transmitting unit 602 executes a parameter transmitting process to transmit the layer 1 parameters received by the parameter receiving unit 601 to the calculation server 620. The estimation result receiving unit 603 receives an estimation result of the wireless quality of the wireless terminal 110 notified from the calculation server 620. The estimation result transmitting unit 604 executes an estimation result transmitting process to transmit the estimation result of the wireless quality of the wireless terminal 110 received by the estimation result receiving unit 603 to the wireless terminal 110.

[0046] 6, the calculation server 620 is the wireless quality estimation device according to this embodiment. Note that the functional components, such as the acquisition unit 121, the parameter estimation unit 122, the wireless quality estimation unit 123, the notification unit 124, and the storage unit 125, may be provided separately in the wireless base station 610 and the calculation server 620, or may be provided separately in a plurality of server devices. In this way, the wireless quality estimation system 100 according to this embodiment can employ various system configurations.

[0047] <Processing Flow> Next, the processing flow of the wireless quality estimation method according to this embodiment will be described.

[0048] 7 is a flowchart showing an example of a wireless quality estimation process according to this embodiment. The following description will be given assuming that the wireless quality estimation system 100 has the system configuration shown in FIG.

[0049] In step S701, the acquisition unit 121 acquires the layer 1 parameters of the wireless terminal 110 to be transmitted by the wireless terminal 110. As a specific example, the acquisition unit 121 may acquire RSRP and RSSI as the layer 1 parameters of the wireless terminal 110, as shown in FIG.

[0050] In step S702, the parameter estimation unit 122 estimates a first Layer 2 parameter that does not depend on the network usage status, based on the Layer 1 parameter acquired by the acquisition unit 121. As a specific example, the parameter estimation unit 122 may calculate an MCS as the first Layer 2 parameter by machine learning from the RSRP and RSSI acquired by the acquisition unit 121, as shown in FIG.

[0051] For example, the parameter estimation unit 122 estimates the MCS estimation result by inputting the RSRP and RSSI acquired by the acquisition unit 121 into a machine learning model 302 that has been trained in advance to estimate the MCS from the RSRP and RSSI.

[0052] In step S703, the wireless quality estimator 123 estimates the wireless quality of the wireless terminal 110 using the Layer 1 parameters acquired by the acquirer 121, the first Layer 2 parameters estimated by the parameter estimator 122, and statistics of the second Layer 2 parameters. As a specific example, the wireless quality estimator 123 may estimate the throughput of the wireless terminal 110 using a machine learning model 501 that has been trained in advance to estimate throughput using a CDF 801 of the RSRP, RSSI, MCS, and number of RBs, as shown in Fig. 8. In the example of Fig. 8, the wireless quality estimator 123 calculates a throughput 802 calculated for each CDF of the number of RBs.

[0053] In this way, if the wireless quality estimation system 100 prepares the machine learning models 302, 501 and past statistical values ​​of the second layer 2 parameters in advance, it becomes possible to predict the wireless quality of the wireless terminal 110 in a comprehensive manner using only the easily obtainable layer 1 parameters.

[0054] (Example of Estimation Results) Fig. 9 is a diagram (1) showing an example of estimation results according to this embodiment. This graph 800 shows the estimated results and actual measurement results of throughput when the median of the CDF 801 of the number of RBs, which is the second Layer 2 parameter, is used in the embodiment shown in Fig. 8. Looking at graph 800, it can be seen that the estimated values ​​are also low throughput at times when the actual measured values ​​are low throughput, and that the overall trend of throughput has been appropriately estimated.

[0055] Furthermore, the wireless quality estimation system 100 may estimate a probabilistic estimate of wireless quality using a cumulative probability density value (1%, 10%, 50%, etc.) of a CDF 801 of the number of RBs, which is a second Layer 2 parameter, as shown in Fig. 10. Furthermore, the wireless quality estimation system 100 may estimate a probability of achieving a predetermined wireless quality using a cumulative probability density value (1%, 10%, 50%, etc.) of a CDF 801 of the number of RBs, for example, and output the result as an estimated result of wireless quality.

[0056] <Hardware Configuration Example> The wireless terminal 110, the wireless base stations 120 and 610, and the calculation server 620 each have the hardware configuration of a computer 1100 as shown in Fig. 11. The calculation server 620 may be configured by a plurality of computers 1100, or may be realized by a program executed on a virtual machine on the cloud.

[0057] 11 is a diagram showing an example of the hardware configuration of a computer. The computer 1100 includes, for example, a processor 1101, a memory 1102, a storage device 1103, a communication device 1104, an input device 1105, an output device 1106, and a bus B.

[0058] The processor 1101 is, for example, an arithmetic unit such as a CPU (Central Processing Unit) that executes predetermined programs to realize various functions. The memory 1102 is a storage medium readable by the computer 1100 and includes, for example, a RAM (Random Access Memory) and a ROM (Read Only Memory). The storage device 1103 is a computer-readable storage medium and may include, for example, a HDD (Hard Disk Drive), an SSD (Solid State Drive), various optical disks, and magneto-optical disks.

[0059] The communication device 1104 includes one or more pieces of hardware (communication devices) for communicating with other devices via a wireless or wired network. The input device 1105 is an input device (e.g., a keyboard, a mouse, a microphone, a switch, a button, a sensor, etc.) that accepts input from the outside. The output device 1106 is an output device (e.g., a display, a speaker, an LED lamp, etc.) that outputs to the outside.

[0060] The bus B is commonly connected to the above components and transmits, for example, address signals, data signals, and various control signals. The processor 1101 is not limited to a CPU, and may be, for example, a DSP (Digital Signal Processor), a PLD (Programmable Logic Device), or an FPGA (Field Programmable Gate Array).

[0061] (Supplementary Note) The wireless terminal 110, wireless base stations 120 and 610, and calculation server 620 in this embodiment may be realized not only by dedicated devices but also by general-purpose computers. In this case, a program for realizing the functions may be recorded on a computer-readable recording medium, and the program recorded on the recording medium may be read into and executed by a computer system. Note that the term "computer system" here includes hardware such as an OS and peripheral devices.

[0062] Furthermore, "computer-readable recording media" includes various storage devices such as portable media such as flexible disks, optical magnetic disks, ROMs, and CD-ROMs, as well as hard disks built into computer systems. Furthermore, "computer-readable recording media" may also include devices that dynamically store programs for a short period of time, such as communication lines when transmitting programs via networks such as the Internet or communication lines such as telephone lines, and devices that store programs for a certain period of time, such as volatile memory within computer systems that serve as servers or clients in such cases.

[0063] Furthermore, the above program may be one that realizes part of the above-mentioned functions, or may be one that can realize the above-mentioned functions in combination with a program already recorded in a computer system, or may be one that is realized using hardware such as a PLD (Programmable Logic Device) or FPGA (Field Programmable Gate Array).

[0064] <Effects of the embodiment> According to the wireless quality estimating system 100 of the present embodiment, it is possible to estimate wireless quality such as throughput that changes depending on the usage status of a wireless communication network.

[0065] Furthermore, if the estimation method for the first layer 2 parameter and the statistical values ​​of the second layer 2 parameter are acquired in advance, it becomes possible to estimate communication quality in a comprehensive manner using only the layer 1 parameters, which can be easily acquired.

[0066] The estimation results can be used, for example, to select a route for an autonomous vehicle, control the video codec of remote control video, and / or network control (priority allocation of RBs, antenna tilt angle control, etc.).

[0067] Summary of Embodiments This specification discloses at least the following wireless quality estimation system, wireless quality estimation device, wireless quality estimation method, and program: (1) A wireless quality estimation system comprising: an acquisition unit that acquires Layer 1 parameters of a wireless terminal; a parameter estimation unit that estimates a first Layer 2 parameter that is independent of network usage status based on the Layer 1 parameters; and a wireless quality estimation unit that estimates the wireless quality of the wireless terminal based on past statistics of the Layer 1 parameter, the first Layer 2 parameter, and a second Layer 2 parameter that is dependent on network usage status. (2) A wireless quality estimation device comprising: an acquisition unit that acquires Layer 1 parameters of a wireless terminal; a parameter estimation unit that estimates a first Layer 2 parameter that is independent of network usage status based on the Layer 1 parameters; and a wireless quality estimation unit that estimates the wireless quality of the wireless terminal based on past statistics of the Layer 1 parameter, the first Layer 2 parameter, and a second Layer 2 parameter that is dependent on network usage status. (Clause 3) A wireless quality estimation method in which a computer executes the following processes: acquiring layer 1 parameters of a wireless terminal, estimating a first layer 2 parameter that is independent of network usage status based on the layer 1 parameters, and estimating wireless quality of the wireless terminal based on past statistics of the layer 1 parameters, the first layer 2 parameter, and a second layer 2 parameter that is dependent on network usage status. (Clause 4) A program, or a storage medium storing a program, that causes a computer to execute the wireless quality estimation method described in clause 3.

[0068] Although the present embodiment has been described above, the present invention is not limited to such a specific embodiment, and various modifications and changes are possible within the scope of the gist of the present invention described in the claims.

[0069] REFERENCE SIGNS LIST 100 Wireless quality estimation system 110 Wireless terminal 120 Wireless base station (wireless quality estimation device) 121 Acquisition unit 122 Parameter estimation unit 123 Wireless quality estimation unit 610 Wireless base station 620 Calculation server (wireless quality estimation device) 1100 Computer

Claims

1. A radio quality estimation system, comprising: an acquisition unit that acquires layer 1 parameters of a wireless terminal; a parameter estimation unit that estimates a first layer 2 parameter independent of network usage status based on the layer 1 parameters; and a radio quality estimation unit that estimates the radio quality of the wireless terminal based on the layer 1 parameters, the first layer 2 parameters, and past statistical values of second layer 2 parameters that depend on network usage status.

2. A radio quality estimation apparatus, comprising: an acquisition unit that acquires layer 1 parameters of a wireless terminal; a parameter estimation unit that estimates a first layer 2 parameter independent of network usage status based on the layer 1 parameters; and a radio quality estimation unit that estimates the radio quality of the wireless terminal based on the layer 1 parameters, the first layer 2 parameters, and past statistical values of second layer 2 parameters that depend on network usage status.

3. A radio quality estimation method, comprising: a computer executing a process of acquiring layer 1 parameters of a wireless terminal; a process of estimating a first layer 2 parameter independent of network usage status based on the layer 1 parameters; and a process of estimating the radio quality of the wireless terminal based on the layer 1 parameters, the first layer 2 parameters, and past statistical values of second layer 2 parameters that depend on network usage status.

4. A program for causing a computer to execute the radio quality estimation method according to claim 3.

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