Communication quality prediction system and communication quality prediction method
The communication quality prediction system improves accuracy by integrating environmental and machine learning models to account for surrounding wireless terminals and base stations, addressing the limitations of conventional prediction methods.
Patent Information
- Authority / Receiving Office
- WO · WO
- Patent Type
- Applications
- Current Assignee / Owner
- NT T INC
- Filing Date
- 2024-12-09
- Publication Date
- 2026-06-18
Smart Images
Figure JP2024043440_18062026_PF_FP_ABST
Abstract
Description
Communication Quality Prediction System and Communication Quality Prediction Method 【0001】 The present invention relates to a communication quality prediction system and a communication quality prediction method. 【0002】 A technique for predicting the communication quality (e.g., throughput) of wireless communication at a prediction target position from quality information (e.g., received power) of wireless communication measured in a prediction target interval is known (see, for example, Non-Patent Document 1). 【0003】 Wakao et al., "Quality Prediction Technology for Optimal Use of Multiple Wireless Accesses", NTT Technical Journal, April 2020. 【0004】 In the conventional technique, the communication quality at the prediction target position is predicted only from the quality information of wireless communication measured in the prediction target interval. However, the communication quality is affected not only by the wireless communication in the prediction target interval but also by the number of peripheral terminals outside the prediction target interval and the communication status, etc. Therefore, there is a problem that the conventional technique cannot predict the communication quality of wireless communication at the prediction target position with high accuracy. 【0005】 An embodiment of the present invention has been made in view of the above problems, and enables the communication quality of wireless communication at a prediction target position to be predicted with higher accuracy. 【0006】 In order to solve the above problems, a communication quality prediction system according to an embodiment of the present invention is a communication quality prediction system that predicts the communication quality of wireless communication between a wireless base station and a wireless terminal, and includes an acquisition unit that acquires information on the wireless environment around the wireless base station and / or the wireless terminal, and a prediction unit that predicts the communication quality at the prediction target position using the data rate of the physical layer of the wireless communication at the prediction target position and the information on the wireless environment. 【0007】 According to an embodiment of the present invention, the communication quality of wireless communication at a prediction target position can be predicted with higher accuracy. 【0008】This figure shows an example of the system configuration of the communication quality prediction system according to this embodiment. This figure shows an example of the functional configuration of the communication quality prediction device according to Embodiment 1. This figure is for explaining the learning device according to Embodiment 1. This flowchart shows an example of the communication quality prediction process according to Embodiment 1. This figure shows an example of the functional configuration of the communication quality prediction device according to Embodiment 2. This figure is for explaining the learning device according to Embodiment 2. This flowchart shows an example of the communication quality prediction process according to Embodiment 2. This figure shows an example of the functional configuration of the communication quality prediction device according to Embodiment 3. This figure is for explaining the learning device according to Embodiment 3. This flowchart shows an example of the communication quality prediction process according to Embodiment 3. This figure shows an example of the computer hardware configuration. This figure shows an example of a conventional communication quality prediction method. 【0009】 Hereinafter, embodiments of the present invention (this embodiment) will be described with reference to the drawings. The embodiments described below are merely examples, and the embodiments to which the present invention is applied are not limited to the embodiments described below. 【0010】 <Overview> Before describing the communication quality prediction method according to this embodiment, we will first describe an example of a conventional communication quality prediction method. 【0011】 For example, in the conventional technology shown in Non-Patent Document 1, the communication quality of wireless communication at the prediction target location (e.g., throughput) is predicted from wireless communication quality information (e.g., received power) measured in the prediction target section. 【0012】 For example, in a wireless environment 1 as shown in Figure 12, a wireless base station 10 and a wireless terminal 20 are performing wireless communication, and the communication quality of the wireless communication at the target location (for example, the location of the wireless terminal 20) is to be predicted. In this case, conventional technology predicts the communication quality, such as throughput, at the target location from wireless information such as received power measured in the target section 2. 【0013】 However, the communication quality at the target location is affected by factors such as the number of other wireless terminals 21 in the vicinity, such as other wireless terminals 21a, 21b, and 21c, or their communication status. Therefore, with conventional technology, it is difficult to predict the communication quality at the target location with high accuracy. 【0014】 Therefore, in order to predict the communication quality of wireless communication at the target location with higher accuracy, the communication quality prediction system according to this embodiment has a system configuration such as that shown in Figure 1. 【0015】 <System Configuration> Figure 1 shows an example of the system configuration of the communication quality prediction system according to this embodiment. The communication quality prediction system 100 according to this embodiment has a communication quality prediction device 110 that acquires wireless information of the prediction target section 2 and information of the surrounding wireless environment 1, and predicts the communication quality of wireless communication at the prediction target location. 【0016】 The radio information for prediction target section 2 includes, for example, information such as RSSI (Received Signal Strength Indicator), PHY (Physical Layer) rate, or throughput. RSSI is information indicating the strength of the received signal in radio communication. RSSI is an example of information on received power. PHY rate is the data rate of the physical layer in the radio section and indicates the amount of data that can be transmitted per unit time. Throughput is information indicating the amount of data actually transmitted per unit time in the radio section. For example, even if multiple radio terminals are communicating with one radio base station at a certain PHY rate, the throughput of each radio terminal will be less than the PHY rate because they are sharing the communication path in the radio section. 【0017】 Information about the surrounding wireless environment 1 includes, for example, information about the number of other wireless terminals 21a, 21b and other wireless base stations 11 around the wireless base station 10, or information about interference between other wireless terminals 21a, 21b and other wireless base stations 11, as shown in Figure 1. The communication quality prediction device 110 acquires information about the surrounding wireless environment 1 from, for example, the wireless base station 10 and / or the wireless information collection terminal 30. Note that the number of other wireless terminals 21a, 21b, the number of other wireless base stations 11, and the number of wireless information collection terminals 30 shown in Figure 1 are examples and may be other numbers. 【0018】 Next, the communication quality prediction system 100 will be described in detail with reference to several examples. 【0019】 [Example 1] <Functional Configuration> Figure 2 is a diagram showing an example of the functional configuration of a communication quality prediction device according to Example 1. The communication quality prediction device 110 is equipped with one or more computers, and by executing a predetermined program on one or more computers, it realizes each of the functional configurations shown in Figure 2, for example. In the example in Figure 2, the communication quality prediction device 110 has an acquisition unit 201, a prediction unit 202, a first learner 203, and a second learner 204, etc. At least a part of each of the above functional configurations may be realized by hardware. 【0020】 The acquisition unit 201 performs an acquisition process to acquire wireless information for the prediction target section 2 and information about the wireless environment 1 surrounding the wireless base station 10 and / or wireless terminal 20. Here, as an example, the following explanation assumes that the wireless environment 1 is a wireless LAN (Local Area Network) environment, but the wireless environment 1 may be other wireless environments. 【0021】 In Embodiment 1, the acquisition unit 201 acquires the RSSI of the predicted location as wireless information for the prediction target section 2. For example, the acquisition unit 201 acquires the RSSI value of the wireless communication at the predicted location measured by the wireless terminal 20 via the wireless base station 10. Alternatively, the acquisition unit 201 may acquire the RSSI value of the wireless communication at the predicted location from the wireless information collection terminal 30. 【0022】 Furthermore, the acquisition unit 201 acquires interference airtime and interference RSSI as information about the surrounding wireless environment 1. Note that interference airtime and interference RSSI are examples of information regarding interference between other wireless terminals 21a, 21b and other wireless base stations 11. 【0023】 Interference airtime is information indicating the time during which other wireless terminals 21a, 21b, and other wireless base stations 11, etc., transmit radio waves and occupy the communication channel. Interference RSSI is information indicating the strength of the received signals received from other wireless terminals 21a, 21b, and other wireless base stations 11, etc. The acquisition unit 201 acquires interference airtime and interference RSSI from the wireless base station 10 or the wireless information collection terminal 30, etc. 【0024】 The prediction unit 202 performs a prediction process to predict the communication quality of wireless communication at the prediction target location, based on the wireless information of the prediction target section 2 acquired by the acquisition unit 201 and the information of the wireless environment 1 surrounding the wireless base station 10 and / or wireless terminal 20. 【0025】 In Embodiment 1, the prediction unit 202 inputs the RSSI of the target location acquired by the acquisition unit 201 to a trained first learner 203 to predict the PHY rate of the target location. The prediction unit 202 also inputs the predicted PHY rate of the target location, the interference airtime, and the interference RSSI to a trained second learner 204 to predict the throughput at the target location. Throughput is an example of the communication quality of wireless communication predicted by the communication quality prediction system 100. 【0026】 Figure 3 is a diagram illustrating the learning device according to Embodiment 1. The first learning device 203 has a first prediction model that models the relationship between the RSSI of a wireless terminal received by a wireless base station and the PHY rate obtained at that time. For example, the first learning device 203 has a first prediction model which is a machine learning model that has been machine-trained using multiple training data sets in which the RSSI of the prediction target interval is used as a feature and the PHY rate of the prediction target interval is assigned as a label. 【0027】 As a result, the prediction unit 202 can, for example, input the RSSI of the prediction target position to the first learner 203, as shown in Figure 2, thereby causing the first learner 203 to output the PHY rate of the prediction target position. 【0028】 The second learner 204 has a second prediction model that models the relationship between the PHY rate of a wireless terminal, the interference airtime and interference RSSI of other surrounding wireless terminals and other wireless base stations, and throughput. For example, the second learner 204 has a second prediction model that is a machine learning model that uses multiple training data sets in which the PHY rate, interference airtime, and interference RSSI of the prediction target interval are used as features and throughput is assigned as a label. 【0029】Furthermore, in the example shown in Figure 2, the PHY rate of the predicted target location predicted by the first learner 203 is input to the second learner 204. As a result, the prediction unit 202 can input the interference airtime and interference RSSI to the second learner 204, causing the second learner 204 to output a predicted throughput value at the predicted target location. 【0030】 <Processing Flow> Figure 4 is a flowchart showing an example of the communication quality prediction process according to Embodiment 1. This process shows an example of the communication quality prediction process executed by a communication quality prediction device 110 having the functional configurations described in Figure 2. At the start of the process in Figure 4, the first learner 203 and the second learner 204 are assumed to have been trained. 【0031】 In step S401, the acquisition unit 201 acquires the RSSI of the prediction target location, the interference airtime, and the interference RSSI. For example, if the prediction target location is the location of the wireless terminal 20, the acquisition unit 201 acquires the RSSI measured by the wireless terminal 20 from the wireless base station 10. If the prediction target location is the location of the wireless information collection terminal 30, the acquisition unit 201 may acquire the RSSI of the wireless base station 10 collected by the wireless information collection terminal 30 from the wireless information collection terminal 30. 【0032】 Furthermore, the acquisition unit 201 acquires interference airtime and interference RSSI collected by the radio base station 10 from other radio terminals 21a, 21b, and other radio base stations 11, etc. Alternatively, the acquisition unit 201 may obtain interference airtime and interference RSSI from radio information collected from the radio base station 10 from other radio terminals 21a, 21b, and other radio base stations 11, etc. In addition, the acquisition unit 201 may acquire interference airtime and interference RSSI collected by the radio information collection terminal 30 from other radio terminals 21a, 21b, and other radio base stations 11, etc. 【0033】In step S402, the prediction unit 202 predicts the PHY rate of the prediction target location from the RSSI of the prediction target location acquired by the acquisition unit 201. For example, the prediction unit 202 inputs the RSSI of the prediction target location acquired by the acquisition unit 201 to the first learner 203, causing the first learner 203 to output the PHY rate at the prediction target location. 【0034】 In step S403, the prediction unit 202 predicts the throughput of the target location from the PHY rate of the target location output by the first learner 203, the interference air time acquired by the acquisition unit 201, and the interference RSSI. For example, following the processing in step S402, the prediction unit 202 inputs the interference air time and interference RSSI acquired by the acquisition unit 201 to the second learner 204, causing the second learner 204 to output the throughput at the target location. 【0035】 In step S404, the communication quality prediction device 110 outputs the predicted throughput value at the prediction target location, which is the prediction result, to a predetermined output destination. In the example in Figure 2, the second learner 204 outputs the predicted throughput value, but this is just one example. The prediction result output may also be performed by the prediction unit 202. The communication quality prediction device 110 may also have an output unit that outputs the prediction result to a predetermined output destination (for example, a wireless base station 10 or a wireless terminal 20, etc.). 【0036】 As described above in Example 1, the communication quality prediction device 110 acquires the RSSI, interference airtime, and interference RSSI of the prediction target location, and can predict the wireless communication throughput at the prediction target location with higher accuracy. 【0037】Note that the system configuration of the communication quality prediction system 100 shown in Figure 1 is just one example. For example, in Figure 1, each functional configuration of the communication quality prediction device 110 may be distributed and provided in multiple devices. Also, in Figure 2, each functional configuration of the communication quality prediction device 110 may be provided in other devices such as the wireless base station 10. If the wireless base station 10 has each functional configuration of the communication quality prediction device 110 in Figure 1, then the wireless base station 10 becomes the communication quality prediction device according to this embodiment. In short, in Figure 1, each functional configuration of the communication quality prediction device 110 only needs to be provided by the communication quality prediction system 100. 【0038】 [Example 2] <Functional Configuration> Figure 5 is a diagram showing an example of the functional configuration of a communication quality prediction device according to Example 2. The communication quality prediction device 110 is equipped with one or more computers, and by executing a predetermined program on one or more computers, it realizes each of the functional configurations shown in Figure 5, for example. In the example of Figure 5, the communication quality prediction device 110 has an acquisition unit 201, a prediction unit 202, a conversion unit 501, and a learner 502, etc. At least a part of each of the above functional configurations may be realized by hardware. 【0039】 The acquisition unit 201 performs an acquisition process to acquire wireless information for the prediction target section 2 and information about the wireless environment 1 surrounding the wireless base station 10 and / or wireless terminal 20. Here, as an example, the following explanation assumes that the wireless environment 1 is a wireless LAN environment, but the wireless environment 1 may be any other wireless environment. 【0040】 In Embodiment 2, the acquisition unit 201 acquires the RSSI of the prediction target location as wireless information for the prediction target section 2, similar to Embodiment 1. The acquisition unit 201 also acquires the number of other wireless terminals 21a, 21b and the number of other wireless base stations 11 as information about the surrounding wireless environment 1 from the wireless base station 10 or the wireless information collection terminal 30, etc. 【0041】Note that the number of other wireless terminals 21a and 21b and the number of other wireless base stations 11 are examples of information regarding the number of other wireless terminals and the number of other wireless base stations around the wireless base station. For example, the information regarding the number of other wireless terminals and the number of other wireless base stations around may include the number of the wireless base station 10 and the wireless terminals 20. Further, the information regarding the number of other wireless terminals and the number of other wireless base stations around the wireless base station may be, for example, the number of the wireless terminals 20 with which the wireless base station 10 and the other wireless base stations 11 are communicating. 【0042】 The prediction unit 202 executes a prediction process for predicting the communication quality of wireless communication at the prediction target position based on the wireless information of the prediction target section 2 acquired by the acquisition unit 201 and the information of the wireless environment 1 around the wireless base station 10 and / or the wireless terminals 20. 【0043】 In the second embodiment, the prediction unit 202 predicts the PHY rate at the prediction target position by converting the RSSI at the prediction target position acquired by the acquisition unit 201 into the PHY rate at the prediction target position using the conversion unit 501. 【0044】 Further, the prediction unit 202 inputs the predicted PHY rate at the prediction target position, the number of other wireless terminals, and the number of other wireless base stations to the learned second learning device 204 to predict the throughput at the prediction target position. Note that the throughput is an example of the communication quality of wireless communication predicted by the communication quality prediction system 100. 【0045】 The conversion unit 501 executes a conversion process for converting the RSSI at the prediction target position into the PHY rate at the prediction target position. Note that the conversion unit 501 may convert the RSSI at the prediction target position acquired by the acquisition unit 201 into the PHY rate at the prediction target position without passing through the prediction unit 202. 【0046】For example, the conversion unit 501 obtains the PHY rate of the prediction target position by a calculation formula using information such as the MCS (Modulation and Coding Scheme) of wireless communication or the maximum throughput value, and the RSSI of the prediction target position. Alternatively, the conversion unit 501 may convert the RSSI of the prediction target position into the PHY rate of the prediction target position using correspondence information or the like in which the RSSI and the PHY rate are associated and stored for each MCS. Note that the MCS indicates a combination of a data modulation method and a channel coding rate. Usually, the larger the MCS number, the larger the transport block size and the higher the throughput. 【0047】 FIG. 6 is a diagram for explaining the learning device according to the second embodiment. The learning device 502 has a prediction model that models the relationship between the PHY rate of a wireless terminal, the number of other wireless terminals in the vicinity, the number of other wireless base stations, and the throughput. For example, the learning device 502 is a prediction model, which is a machine learning model obtained by performing machine learning using a plurality of learning data in which the PHY rate of the prediction target section, the number of other wireless terminals, and the number of other wireless base stations are used as feature amounts and the throughput is used as a label. 【0048】 Also, in the example of FIG. 5, the PHY rate of the prediction target position converted by the conversion unit 501 is input to the learning device 502. Thereby, the prediction unit 202 can cause the learning device 502 to output a predicted value of the throughput at the prediction target position by inputting the number of other wireless terminals and the number of other wireless base stations to the learning device 502. 【0049】 Note that the configuration of the communication quality prediction device 110 shown in FIG. 5 is an example. For example, the communication quality prediction device 110 may have the first learning device 203 described in the first embodiment instead of the conversion unit 501. Further, the function of the conversion unit 501 of the communication quality prediction device 110 may be possessed by the prediction unit 202. 【0050】<Processing Flow> Figure 7 is a flowchart showing an example of the communication quality prediction process according to Embodiment 2. This process shows an example of the communication quality prediction process executed by a communication quality prediction device 110 having the functional configurations described in Figure 5. At the start of the process in Figure 7, the learner 502 is assumed to have already been trained. 【0051】 In step S701, the acquisition unit 201 acquires the RSSI of the prediction target location, the number of other wireless terminals, and the number of other wireless base stations. For example, the acquisition unit 201 acquires the RSSI of the prediction target location in the same manner as in Embodiment 1. The acquisition unit 201 also acquires, for example, the number of other wireless terminals 21a, 21b, and the number of other wireless base stations 11 from the wireless base station 10. Alternatively, the acquisition unit 201 may acquire the number of wireless terminals in the wireless environment 1 and the number of wireless base stations from the wireless information collection terminal 30 to determine the number of other wireless terminals and the number of other wireless base stations. 【0052】 In step S702, the prediction unit 202 predicts the PHY rate at the prediction target location from the RSSI of the prediction target location. For example, the prediction unit 202 inputs the RSSI of the prediction target location acquired by the acquisition unit 201 to the conversion unit 501, causing the conversion unit 501 to output the PHY rate at the prediction target location. 【0053】 In step S703, the prediction unit 202 predicts the throughput at the prediction target location based on the PHY rate of the prediction target location output by the conversion unit 501, the number of other wireless terminals acquired by the acquisition unit 201, and the number of other wireless base stations. For example, following the processing in step S702, the prediction unit 202 inputs the number of other wireless terminals acquired by the acquisition unit 201 and the number of other wireless base stations to the learner 502, causing the learner 502 to output the throughput at the prediction target location. 【0054】In step S704, the communication quality prediction device 110 outputs the predicted throughput value at the target location, which is the prediction result, to a predetermined output destination. In the example in Figure 5, the learner 502 outputs the predicted throughput value, but this is just one example. The prediction result output may also be performed by the prediction unit 202. The communication quality prediction device 110 may also have an output unit that outputs the prediction result to a predetermined output destination (for example, a wireless base station 10 or a wireless terminal 20, etc.). 【0055】 As described above, according to Embodiment 2, the communication quality prediction device 110 acquires the RSSI of the prediction target location, the number of other wireless terminals in the vicinity, and the number of other wireless base stations, and can predict the wireless communication throughput at the prediction target location with higher accuracy. 【0056】 [Example 3] <Functional Configuration> Figure 8 is a diagram showing an example of the functional configuration of a communication quality prediction device according to Example 3. The communication quality prediction device 110 is equipped with one or more computers, and by executing a predetermined program on one or more computers, it realizes each of the functional configurations shown in Figure 8, for example. In the example of Figure 8, the communication quality prediction device 110 has an acquisition unit 201, a prediction unit 202, a learner 801, and a subtraction unit 802, etc. At least a part of each of the above functional configurations may be realized by hardware. 【0057】 The acquisition unit 201 performs an acquisition process to acquire wireless information for the prediction target section 2 and information about the wireless environment 1 surrounding the wireless base station 10 and / or wireless terminal 20. Here, as an example, the following explanation assumes that the wireless environment 1 is a wireless LAN environment, but the wireless environment 1 may be any other wireless environment. 【0058】 In Embodiment 3, the acquisition unit 201 acquires the PHY rate of the prediction target location as wireless information for the prediction target section 2. Thus, the communication quality prediction device 110 may acquire the PHY rate of the prediction target location from an external source. For example, the acquisition unit 201 may acquire the PHY rate of the prediction target location from a wireless base station 10 having the first learner 203 of Embodiment 1 or the conversion unit 501 of Embodiment 2, or from a wireless information collection terminal 30, etc. 【0059】 Furthermore, the acquisition unit 201 acquires interference airtime and interference RSSI as information about the surrounding wireless environment 1, in the same manner as in Embodiment 1. Note that interference airtime and interference RSSI are examples of information regarding interference between other wireless terminals 21a, 21b and other wireless base stations 11. 【0060】 The prediction unit 202 performs a prediction process to predict the communication quality of wireless communication at the prediction target location, based on the wireless information of the prediction target section 2 acquired by the acquisition unit 201 and the information of the wireless environment 1 surrounding the wireless base station 10 and / or wireless terminal 20. 【0061】 In Example 3, the prediction unit 202 inputs the interference airtime and interference RSSI acquired by the acquisition unit 201 to the learner 801 to predict the throughput degradation amount. The prediction unit 202 also subtracts the throughput degradation amount predicted by the learner 801 from the PHY rate of the target location acquired by the acquisition unit 201 to predict the throughput value at the target location. Throughput is an example of the communication quality of wireless communication predicted by the communication quality prediction system 100. 【0062】 Figure 9 is a diagram illustrating the learning device according to Embodiment 3. The learning device 801 has a predictive model that models the relationship between interference airtime and interference RSSI caused by other surrounding wireless terminals and other wireless base stations, and throughput degradation, which is the difference between the PHY rate and throughput of the wireless terminal in that situation. For example, the learning device 801 has a predictive model which is a machine learning model that has been machine-trained using multiple training data sets in which interference airtime and interference RSSI are used as features and throughput degradation is assigned as a label. 【0063】 Furthermore, in the example shown in Figure 8, the throughput degradation amount predicted by the learner 801 is input to the subtraction unit 802. As a result, the prediction unit 202 can input the PHY rate of the prediction target location acquired by the acquisition unit 201 to the subtraction unit 802, thereby causing the subtraction unit 802 to output a predicted throughput value at the prediction target location. 【0064】Note that the configuration of the communication quality prediction device 110 shown in Figure 8 is just one example. For example, the function of the subtraction unit 802 in the communication quality prediction device 110 may be provided by the prediction unit 202. Also, the PHY rate of the prediction target location may be predicted by the communication quality prediction device 110 using the first learner 203 from the RSSI of the prediction target location, as in Example 1. Alternatively, the PHY rate of the prediction target location may be predicted by the communication quality prediction device 110 using the conversion unit 501 from the RSSI of the prediction target location, as in Example 2. 【0065】 <Processing Flow> Figure 10 is a flowchart showing an example of the communication quality prediction process according to Embodiment 3. This process shows an example of the communication quality prediction process executed by a communication quality prediction device 110 having the functional configurations described in Figure 8. At the start of the process in Figure 10, the learner 801 is assumed to have already been trained. 【0066】 In step S1001, the acquisition unit 201 acquires the PHY rate, interference airtime, and interference RSSI of the prediction target location. For example, the acquisition unit 201 acquires the PHY rate of the prediction target location from the wireless base station 10 or the wireless information collection terminal 30, etc. The acquisition unit 201 also acquires the interference airtime and interference RSSI from the wireless base station 10 or the wireless information collection terminal 30, etc., in the same manner as in Embodiment 1. 【0067】 In step S1002, the prediction unit 202 predicts the throughput degradation amount from the interference air time and interference RSSI acquired by the acquisition unit 201. For example, the prediction unit 202 inputs the interference air time and interference RSSI acquired by the acquisition unit 201 to the learner 801, causing the learner 801 to output the throughput degradation amount. 【0068】In step S1003, the prediction unit 202 predicts the put at the target location based on the PHY rate of the target location acquired by the acquisition unit 201 and the throughput degradation amount predicted by the learner 801. For example, following the processing in step S1002, the prediction unit 202 inputs the PHY rate of the target location acquired by the acquisition unit 201 to the subtraction unit 802, causing the subtraction unit 802 to output the throughput at the target location. 【0069】 In step S1004, the communication quality prediction device 110 outputs the predicted throughput value at the prediction target location, which is the prediction result, to a predetermined output destination. In the example in Figure 8, the subtraction unit 802 outputs the predicted throughput value, but this is just one example. The prediction result may also be output by the prediction unit 202. The communication quality prediction device 110 may also have an output unit that outputs the prediction result to a predetermined output destination (for example, a wireless base station 10 or a wireless terminal 20, etc.). 【0070】 As described above, according to Embodiment 3, the communication quality prediction device 110 acquires the PHY rate, interference airtime, and interference RSSI of the prediction target location, and can predict the wireless communication throughput at the prediction target location with higher accuracy. 【0071】 <Hardware Configuration> The communication quality prediction device 110 has a computer hardware configuration as shown in Figure 11, for example. However, the computer is not limited to a physical machine, but may be a virtual machine for the cloud, for example. 【0072】 Figure 11 shows an example of a computer hardware configuration. In the example in Figure 11, the computer 1100 includes a processor 1101, memory 1102, storage device 1103, communication device 1104, input device 1105, output device 1106, and bus B, etc. 【0073】The processor 1101 is a computing device such as a CPU (Central Processing Unit) that realizes various functions by executing a predetermined program. In addition to the CPU, the processor 1101 may also have other processors such as a GPU (Graphics Processing Unit). The memory 1102 is a storage medium that can be read by the computer 1100, and includes, for example, RAM (Random Access Memory) and ROM (Read Only Memory). The storage device 1103 is a large-capacity storage medium that can be read by the computer, and may include, for example, an HDD (Hard Disk Drive), an SSD (Solid State Drive), various optical discs, and magneto-optical discs. 【0074】 The communication device 1104 includes one or more communication devices for communicating with other devices via a wireless or wired network. The input device 1105 is an input device that accepts input from the outside (e.g., a keyboard, mouse, microphone, switch, button, sensor, etc.). The output device 1106 is an output device that outputs to the outside (e.g., a display, speaker, LED lamp, etc.). The input device 1105 and the output device 1106 may be configured as an integrated unit (e.g., an input / output device such as a touch panel display). 【0075】 Bus B is connected in common to all of the above components and transmits, for example, address signals, data signals, and various control signals. Note that the processor 1101 may be other processors such as a DSP (Digital Signal Processor), a PLD (Programmable Logic Device), or an FPGA (Field Programmable Gate Array). 【0076】(Supplement) The communication quality prediction device 110 in this embodiment is not limited to being implemented by a dedicated device, but may also be implemented by a general-purpose computer. In that case, the program for implementing this function may be recorded on a computer-readable recording medium, and the program recorded on this recording medium may be loaded into a computer system and executed. The term "computer system" as used herein includes hardware such as an OS (Operating System) and peripheral devices. 【0077】 Furthermore, "computer-readable recording media" includes various storage devices such as flexible disks, magneto-optical disks, ROMs, CD-ROMs, and hard disks built into computer systems. In addition, "computer-readable recording media" may also include those that dynamically hold programs for a short period of time, such as communication lines used when transmitting programs over networks such as the Internet or communication lines such as telephone lines, and those that hold programs for a certain period of time, such as volatile memory inside computer systems that act as servers or clients in such cases. 【0078】 Furthermore, the above program may be for the purpose of realizing some of the functions described above, or it may be able to realize the above functions in combination with a program already recorded in the computer system, or it may be implemented using hardware such as a PLD or FPGA. 【0079】 <Effects of the Embodiment> According to this embodiment, the communication quality prediction system 100, which predicts the communication quality of wireless communication between a wireless base station 10 and a wireless terminal 20, can predict the communication quality of wireless communication at the prediction target location with higher accuracy. For example, the communication quality prediction system 100 according to this embodiment can improve the accuracy of predicting communication quality by acquiring information on the surrounding wireless environment 1 as well as wireless information of the prediction target section, and predicting the communication quality of wireless communication by taking that information into account. 【0080】<Summary of Embodiments> This specification discloses at least the following communication quality prediction systems, communication quality prediction devices, communication quality prediction methods, and programs. (Section 1) A communication quality prediction system for predicting the communication quality of wireless communication between a wireless base station and a wireless terminal, comprising: an acquisition unit for acquiring information on the wireless environment surrounding the wireless base station and / or the wireless terminal; and a prediction unit for predicting the communication quality at the prediction target location using the data rate of the physical layer of the wireless communication at the prediction target location and the information on the wireless environment. (Section 2) The communication quality prediction system according to Section 1, wherein the prediction unit predicts the data rate of the physical layer of the wireless communication at the prediction target location, which is used for predicting the communication quality, using information on the received power of the wireless communication at the prediction target location. (Section 3) The communication quality prediction system according to Section 1 or 2, wherein the information on the wireless environment includes information on the number of other wireless terminals and the number of other wireless base stations around the wireless base station, or information on interference between the other wireless terminals and the other wireless base stations, and the prediction unit predicts the throughput at the prediction target location. (Clause 4) A communication quality prediction device for predicting the communication quality of wireless communication between a wireless base station and a wireless terminal, comprising: an acquisition unit for acquiring information on the wireless environment surrounding the wireless base station and / or the wireless terminal; and a prediction unit for predicting the communication quality at the prediction target location using the data rate of the physical layer of the wireless communication at the prediction target location and the information on the wireless environment. (Clause 5) A communication quality prediction method comprising: a computer for predicting the communication quality of wireless communication between a wireless base station and a wireless terminal, which performs an acquisition process for acquiring information on the wireless environment surrounding the wireless base station and / or the wireless terminal; and a prediction process for predicting the communication quality at the prediction target location using the data rate of the physical layer of the wireless communication at the prediction target location and the information on the wireless environment.(Clause 6) A program, or a storage medium storing a program, that causes a computer to predict the communication quality of wireless communication between a wireless base station and a wireless terminal to perform an acquisition process to acquire information about the wireless environment around the wireless base station and / or the wireless terminal, and a prediction process to predict the communication quality at the prediction target location using the data rate of the physical layer of the wireless communication at the prediction target location and the information about the wireless environment. 【0081】 Although this embodiment has been described above, the present invention is not limited to this specific embodiment, and various modifications and changes are possible within the scope of the gist of the invention as described in the claims. 【0082】 1 Wireless environment 10 Wireless base station 11 Other wireless base stations 20 Wireless terminals 21, 21a, 21b Other wireless terminals 30 Wireless information collection terminal 100 Communication quality prediction system 110 Communication quality prediction device 201 Acquisition unit 202 Prediction unit 203 First learner 204 Second learner 501 Conversion unit 502, 801 Learner 802 Subtraction unit 1100 Computer
Claims
1. A communication quality prediction system for predicting the communication quality of wireless communication between a wireless base station and a wireless terminal, comprising: an acquisition unit for acquiring information on the wireless environment surrounding the wireless base station and / or the wireless terminal; and a prediction unit for predicting the communication quality at the prediction target location using the data rate of the physical layer of the wireless communication at the prediction target location and the information on the wireless environment.
2. The communication quality prediction system according to claim 1, wherein the prediction unit uses information on the received power of the wireless communication at the prediction target location to predict the data rate of the physical layer of the wireless communication at the prediction target location, which is used for predicting the communication quality.
3. The communication quality prediction system according to claim 1 or 2, wherein the wireless environment information includes information on the number of other wireless terminals and the number of other wireless base stations in the vicinity of the wireless base station, or information on interference between the other wireless terminals and the other wireless base stations, and the prediction unit predicts the throughput at the prediction target location.
4. A communication quality prediction method comprising: a computer predicting the communication quality of wireless communication between a wireless base station and a wireless terminal, which performs an acquisition process to acquire information about the wireless environment around the wireless base station and / or the wireless terminal; and a prediction process to predict the communication quality at the prediction target location using the data rate of the physical layer of the wireless communication at the prediction target location and the information about the wireless environment.