Receiver and radio communication program

Abstract

This disclosure relates to a receiver and a radio communication program. The receiver is for receiving a data signal from a transmitter, and includes a processor and a memory storing a program to be executed by the processor. The processor is configured to execute: processing for performing learning by an autoencoder (AE) on the basis of training data corresponding to transmission data and learning data corresponding to transmission data; processing for storing a learning result; processing for inputting a received data signal to a learning model generated on the basis of the learning result; processing for determining that compensation needs to be performed on the received data signal when a data signal output from the learning model is different from the received data signal; and processing for performing compensation on the received data signal when it is determined that compensation needs to be performed.

Description

Receiver and wireless communication program

[0001] The present disclosure relates to a receiver and a wireless communication program.

[0002] In order to improve communication quality, there is a technology that outputs an original signal based on a signal that has changed during the communication process. For example, Non-Patent Document 1 discloses a method of outputting an original signal based on a pilot signal affected by amplifier distortion and a multiple regression model obtained by pre-learning. This method improves communication quality by deriving the input / output characteristics of the amplifier and generating a reference signal to be used for demodulation based on the derived characteristics.

[0003] T. Tanaka, K. Kuriyama, H. Hasegawa, and T. Miyagi, "An Estimation Method of Input / Output Characteristics of Transmitting Amplifiers Using Multiple Regression Analysis," IEICE General Conference B-5-81, 2023.

[0004] However, the above-mentioned method has a problem in that the amount of shared information increases and communication speed decreases because a pilot signal is used.

[0005] In order to solve the above-mentioned problems, a first object of the present disclosure is to provide a receiver that can improve communication speed.

[0006] A second object of the present disclosure is to provide a wireless communication program that can improve communication speed.

[0007] A first aspect of the present disclosure is a receiver that receives a data signal from a transmitter, and is preferably configured to include a processor and a memory that stores a program to be executed by the processor, and the processor is configured to perform the following processes: learning using an autoencoder (AE) based on teacher data corresponding to the transmitted data and learning data corresponding to the transmitted data; storing the learning results; inputting the received data signal into a learning model generated based on the learning results; determining that the received data signal needs to be compensated if the data signal output from the learning model is different from the received data signal; and compensating the received data signal if it is determined that compensation is necessary.

[0008] A second aspect of the present disclosure is a wireless communication program to be executed by a receiver having a processor and a memory, the program being stored in the memory and computer-readable, and including a program that causes the processor to perform the following processes: learning using an autoencoder (AE) based on teacher data corresponding to the transmitted data and learning data corresponding to the transmitted data; storing the learning results; inputting a received data signal into a learning model generated based on the learning results; determining that the received data signal needs to be compensated if the data signal output from the learning model is different from the received data signal; and compensating the received data signal if it is determined that compensation is necessary.

[0009] According to the first and second aspects of the present disclosure, communication speed can be improved.

[0010] 1 is a diagram illustrating a configuration example of a wireless communication system according to a first embodiment of the present disclosure; FIG. 2 is a diagram illustrating a hardware configuration of a receiver according to a first embodiment of the present disclosure; FIG. 3 is a diagram illustrating pre-processing according to a first embodiment of the present disclosure; FIG. 4 is a diagram illustrating processing according to a first embodiment of the present disclosure when a signal changes during communication; and FIG. 5 is a diagram illustrating processing according to a first embodiment of the present disclosure when a signal does not change during communication.

[0011] 1 is a diagram illustrating a configuration example of a wireless communication system according to a first embodiment of the present disclosure. The wireless communication system 100 includes a transmitter 20. The transmitter 20 transmits a signal to a receiver 40.

[0012] The transmitter 20 has an information generating unit 21. The information generating unit 21 generates information bits for information to be transmitted to the receiver 40. The information generating unit 21 may also have a function of adding an error correction code or an interleaving function.

[0013] The generated information bits are transmitted to the data signal modulation unit 22. The data signal modulation unit 22 modulates the information bits into a data signal. The modulation method used here is, for example, quadrature amplitude modulation (QAM).

[0014] The data signal obtained by the modulation is transmitted to the D / A converter 23. The D / A converter 23 converts the digitally modulated data signal into an analog signal, generating an I signal and a Q signal.

[0015] The analog data signal is transmitted to the IQ signal modulation unit 24. The IQ signal modulation unit 24 performs quadrature modulation on the I signal and the Q signal.

[0016] The quadrature-modulated data signal is transmitted to the amplifier 25. The amplifier 25 amplifies the data signal and transmits it to the receiver 40.

[0017] The receiver 40 includes an amplifier 41. The amplifier 41 amplifies the data signal received from the transmitter 20.

[0018] The amplified data signal is transmitted to the IQ signal demodulation unit 42. The IQ signal demodulation unit 42 demodulates the received data signal into an I signal and a Q signal.

[0019] The demodulated data signal is transmitted to the A / D converter 43. The A / D converter 43 digitizes the analog data signal for digital demodulation.

[0020] The digitized data signal is transmitted to a channel equalization unit 44. The channel equalization unit 44 obtains an estimate of the originally transmitted signal by inversely calculating the amplitude and phase information of the channel response based on the data signal.

[0021] The data signal from which the estimated value has been obtained is transmitted to the determination unit 45. The determination unit 45 generates a learning model based on the learning results of the AE (Autoencoder) obtained in the pre-processing. The determination unit 45 then uses this learning model to determine whether the received data signal needs to be compensated. Details of the pre-processing and the determination method will be described later.

[0022] The imperfection of the RF circuit may be, for example, nonlinear distortion, IQ imbalance, or phase noise of an amplifier. The object of compensation may be imperfections of a plurality of RF circuits.

[0023] If it is determined that compensation is necessary, the data signal is transmitted to the compensator 46. The compensator 46 compensates for RF circuit imperfections in the data signal.

[0024] The compensated data signal and the data signal that does not need to be compensated are transmitted to the information detector 47. The information detector 47 detects information bits from the data signal. Depending on the function of the information generator 21, the information detector 47 may also have a function of decoding error correction codes or a deinterleaving function.

[0025] Next, the pre-processing will be described. The wireless communication system 100 includes a pre-processing unit 60. The pre-processing unit 60 may be included in each receiver 40. Alternatively, one pre-processing unit 60 may be connected to multiple receivers 40, and perform pre-processing collectively.

[0026] The pre-processing unit 60 includes a learning data generating unit 61. The learning data generating unit 61 generates learning data to be used in the DAE.

[0027] The generated learning data is transmitted to the AE learning unit 62. The AE learning unit 62 performs learning by AE based on the learning data.

[0028] The AE learning results are transmitted to a learning result storage unit 48 included in the receiver 40. The learning result storage unit 48 stores the AE learning results obtained by the pre-processing unit 60. The aforementioned determination unit 45 generates a learning model based on the learning results.

[0029] 2 is a diagram illustrating a hardware configuration of a receiver according to the first embodiment of the present disclosure. Each function of the receiver 40 may be partially or entirely configured by hardware such as a programmable logic device (PLD) or a field programmable gate array (FPGA), or may be configured as a program executed by a processor such as a CPU.

[0030] For example, the receiver 40 can be realized using a computer and a program, and the program can be recorded on a storage medium or provided via a network.

[0031] 2, the receiver 40 has an input unit 400, an output unit 401, a communication unit 402, a CPU 403, a memory 404, and an HDD 405 connected via a bus 406, and functions as a computer. The receiver 40 is also capable of inputting and outputting data to and from a computer-readable storage medium 407.

[0032] The input unit 400 is, for example, a keyboard and a mouse, etc. The output unit 401 is, for example, a display device such as a display.

[0033] The communication unit 402 is, for example, a communication interface that communicates with a wireless device to be controlled.

[0034] The CPU 403 controls each component of the receiver 40 and performs predetermined processing, etc. The memory 404 and HDD 405 store data, etc.

[0035] The storage medium 407 is capable of storing programs and the like that cause the receiver 40 to execute the functions of the receiver 40. Note that the architecture that configures the receiver 40 is not limited to the example shown in FIG.

[0036] 3 is a diagram illustrating pre-processing according to the first embodiment of the present disclosure. First, a learning data generation unit 61 generates learning data. The learning data is, for example, a pair of learning data 2 and teacher data 4. The learning data 2 corresponds to transmission data, and is, for example, a complex signal before it is changed due to the influence of imperfections in the RF circuit. The teacher data 4 corresponds to transmission data, and is, for example, a complex signal before it is changed due to the influence of imperfections in the RF circuit. In other words, the learning data 2 and teacher data 4 in the present disclosure are the same signal.

[0037] Next, the AE learning unit 62 performs learning using the AE based on the learning data. The AE learning results are transmitted to the learning result storage unit 48. The learning result storage unit 48 stores the AE learning results. The determination unit 45 generates a learning model based on the AE learning results acquired from the learning result storage unit 48. This learning model is a learning model that generates the same signal from the signal before it is changed due to the influence of imperfections in the RF circuit.

[0038] In this embodiment, AE is used to generate the learning model. When imperfections in multiple RF circuits affect a signal, the effects of each are superimposed, resulting in complex signal changes. Therefore, even when machine learning is used for compensation, it becomes necessary to extract fine features. Therefore, this embodiment employs an autoencoder, which is less susceptible to overfitting and is suitable for extracting fine features.

[0039] In this embodiment, AE is used to determine whether a received data signal needs to be compensated. For example, consider a method of determining whether an input signal is equal to any of the known pre-degraded signals. This method requires storing a large number of signals to prepare the signal points necessary for the determination. On the other hand, in this embodiment, information in the form of restoration results can be obtained using the AE learning model. Therefore, it is possible to determine whether compensation is necessary without having to store a large number of signals.

[0040] 4 is a diagram illustrating processing when a signal changes during communication according to the first embodiment of the present disclosure. During communication, information bits generated by the information generation unit 21 of the transmitter 20 are first transmitted as a data signal 6. Here, processing is shown when the data signal 6 changes due to the influence of imperfections in the RF circuit before it reaches the determination unit 45. Therefore, the determination unit 45 receives the changed data signal 8.

[0041] Therefore, the determination unit 45 inputs the received data signal 8 to a learning model generated based on the learning results. The learning model used here is a learning model that generates the same signal from the signal before it is changed due to the imperfections of the RF circuit. Therefore, if the data signal input to this AE has changed, the features required for restoration will be different, and a data signal different from the input data signal will be output.

[0042] Therefore, if the data signal output from the learning model differs from the received data signal, the determining unit 45 determines that the received data signal needs to be compensated.

[0043] If it is determined that compensation is necessary, the determination unit 45 transmits the data signal to the compensation unit 46. The compensation unit 46 compensates for imperfections of the RF circuit in the data signal. The compensated data signal is transmitted to the information detection unit 47. The information detection unit 47 detects information bits from the data signal.

[0044] 5 is a diagram illustrating processing when a signal does not change during communication according to the first embodiment of the present disclosure. During communication, information bits generated by the information generation unit 21 of the transmitter 20 are first transmitted as a data signal 6. Here, processing is shown for a case where the data signal 6 does not change due to the influence of imperfections in the RF circuit before it reaches the determination unit 45. Therefore, the determination unit 45 receives an unchanged data signal 10.

[0045] Therefore, the determination unit 45 uses a learning model generated in advance to convert the data signal 10. Specifically, the determination unit 45 inputs the received data signal 8 to the AE.

[0046] If the data signal input to the AE has not changed, the characteristics required for restoration are the same, and therefore the same data signal as the input data signal is output. In other words, if the output signal is the same as the input signal, the determination unit 45 can determine that there is no need to compensate the received data signal.

[0047] If it is determined that compensation is not necessary, the determining section 45 transmits the data signal to the information detecting section 47. The information detecting section 47 detects information bits from the data signal.

[0048] As described above, in the present disclosure, whether compensation is necessary can be determined from the received data signal, eliminating the need to use a pilot signal and improving communication speed. Furthermore, because it determines whether compensation is necessary, compensation processing can be reduced if compensation is not necessary. This means that power consumption can be reduced.

[0049] In this embodiment, the influence of imperfections in the RF circuit has been described as an example, but the present invention is effective for all causes of signal changes, such as fading.

[0050] The learning model shown in this embodiment is merely an example, and there are no limitations on the network configuration, learning data, etc. Furthermore, there are no limitations on the configuration of the wireless communication system, etc.

[0051] 2 Learning data 4 Teacher data 6 Data signal 8 Data signal 10 Data signal 20 Transmitter 40 Receiver

Claims

1. A receiver that receives a data signal from a transmitter, comprising a processor and a memory storing a program to be executed by the processor, wherein the processor performs: a process of performing learning by an Autoencoder (AE) based on teacher data corresponding to transmission data and learning data corresponding to the transmission data; a process of storing the learning result; a process of inputting the received data signal to a learning model generated based on the learning result; a process of determining that the received data signal needs to be compensated when the data signal output from the learning model is different from the received data signal; and a process of compensating the received data signal when it is determined that compensation is necessary.

2. The receiver according to claim 1, wherein the learning data and the teacher data are complex signals before being changed by the influence of the incompleteness of the RF circuit.

3. The receiver according to claim 2, wherein the incompleteness of the RF circuit is at least one of non-linear distortion of an amplifier, IQ imbalance, and phase noise.

4. A wireless communication program to be executed by a receiver including a processor and a memory, the program being stored in the memory and being computer-readable, and including a program for causing the processor to perform: a process of performing learning by an Autoencoder (AE) based on teacher data corresponding to transmission data and learning data corresponding to the transmission data; a process of storing the learning result; a process of inputting the received data signal to a learning model generated based on the learning result; a process of determining that the received data signal needs to be compensated when the data signal output from the learning model is different from the received data signal; and a process of compensating the received data signal when it is determined that compensation is necessary.

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