Receiver and wireless communication program

Abstract

This disclosure relates to a receiver and a wireless communication program. This receiver receives data from a transmitter, and comprises a processor and a memory in which a program to be executed by the processor is stored. The processor is configured so as to execute: processing for performing a plurality of types of learning by means of an autoencoder (AE) on the basis of a plurality of types of data for training; processing for storing a plurality of types of training results; processing for determining the compensation factor required for a received data signal by using a plurality of types of trained models generated on the basis of the training results; and processing for compensating the received data signal in accordance with the determined compensation factor. The data for training is constituted by teacher data corresponding to reception data and training data corresponding to the reception data, and the corresponding compensation factor is associated with the data for training.

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 method uses pilot signals, which increases the amount of shared information and reduces communication speed.Furthermore, there is a problem that compensation performance deteriorates significantly when the training data used in pre-training and the received signal differ significantly due to changes in the type or performance of the wireless equipment used in communication.

[0005] In order to solve the above-mentioned problems, the first object of the present disclosure is to provide a receiver that can perform appropriate compensation even when the type or performance of wireless equipment used in communication changes, and that can improve communication speed.

[0006] A second object of the present disclosure is to provide a wireless communication program that can appropriately compensate for changes in the type or performance of wireless devices used in communication and can improve communication speed.

[0007] A first aspect of the present disclosure is a receiver that receives data from a transmitter, the receiver including a processor and a memory that stores a program to be executed by the processor, the processor being configured to perform a process of performing multiple types of learning using an autoencoder (AE) based on multiple types of learning data, a process of storing the multiple types of learning results, a process of determining a compensation factor required for a received data signal using multiple types of learning models generated based on the learning results, and a process of compensating the received data signal in accordance with the determined compensation factor, and the learning data is preferably teacher data corresponding to the received data and learning data corresponding to the received data, and the receiver is linked to the corresponding compensation factor.

[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 for causing the processor to perform a process of performing multiple types of learning using an autoencoder (AE) based on multiple types of learning data, a process of storing the multiple types of learning results, a process of determining compensation factors required for a received data signal using multiple types of learning models generated based on the learning results, and a process of compensating the received data signal in accordance with the determined compensation factors, wherein the learning data is preferably teacher data corresponding to the received data and learning data corresponding to the received data, and the wireless communication program is linked to the corresponding compensation factors.

[0009] According to the first and second aspects of the present disclosure, even when the type or performance of wireless equipment used in communication changes, appropriate compensation can be made and 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; and FIG. 4 is a diagram illustrating processing during communication according to a first embodiment of the present disclosure.

[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 a 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 multiple learning models based on the learning results of multiple AEs (Autoencoders) obtained in the pre-processing. The determination unit 45 then uses the multiple learning models to determine the compensation factor required for the received data signal.

[0022] The compensation factors are factors that cause imperfections in the RF circuit, such as nonlinear distortion of the amplifier, IQ imbalance, phase noise, and combinations thereof. Furthermore, the compensation factors may include "no compensation required," which is used when compensation is not required. Details of the pre-processing and determination methods will be described later.

[0023] The data signal is transmitted to the compensator 46a or 46b depending on the determined compensation factor. The compensators 46a and 46b compensate the received data signal. The compensators 46a and 46b differ in compensation target and calculation capability. While the embodiment shown here has two types of compensators, 46a and 46b, three or more types may be used.

[0024] The compensated data signal is 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 has a learning data generating unit 61. The learning data generating unit 61 generates learning data to be used for AE. Here, multiple types of learning data are generated depending on the compensation factor.

[0027] The generated multiple types of learning data are transmitted to the AE learning unit 62. The AE learning unit 62 performs multiple learning operations using the AE based on the multiple types of learning data.

[0028] The multiple learning results are transmitted to a learning result storage unit 48 included in the receiver 40. The learning result storage unit 48 stores multiple types of learning results obtained by the pre-processing unit 60. The aforementioned determination unit 45 generates multiple types of learning models based on the multiple 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 received data and is, for example, a complex signal that has been changed by a specific compensation factor. The teacher data 4 corresponds to received data and is, for example, a complex signal that has been changed by the same compensation factor as the learning data 2. In other words, the learning data 2 and teacher data 4 in the present disclosure are the same signal.

[0037] Furthermore, the learning data generating unit 61 associates the learning data with the corresponding compensation factor. As described above, the learning data generating unit 61 generates multiple types of learning data associated with each of the multiple types of compensation factors.

[0038] Next, the AE learning unit 62 performs multiple types of learning using the AE based on multiple types of learning data. First, the AE learning unit 62 performs a first learning using a learning network using the AE 10 based on the learning data to generate a learning model. This learning network is, for example, a network with a machine learning layer configuration. This learning model makes it possible to extract feature quantities 16 that occur between the encoder process 12 and the decoder process 14 when the AE 10 outputs teacher data 4 from learning data 2.

[0039] Next, the AE learning unit 62 performs second learning using the extracted feature quantities 16. Specifically, learning is performed using the extracted feature quantities 16 as learning data, and compensation factors and compensation programs corresponding to the corresponding compensation factors as training data. This learning is performed for multiple types of compensation factors, so multiple types of learning results can be obtained.

[0040] The multiple types of learning results are transmitted to the learning result storage unit 48. The learning result storage unit 48 stores the multiple types of learning results. The determination unit 45 generates multiple types of learning models based on the multiple types of learning results acquired from the learning result storage unit 48.

[0041] This learning model generates the same signal from the signal that has been changed by the associated compensation factor. This learning model is generated for each assumed compensation factor.

[0042] 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.

[0043] In this embodiment, AE is used to determine the compensation factor required for a received data signal. For example, consider a method for 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 required for the determination. On the other hand, in this embodiment, information on the restoration results can be obtained using the AE learning model. Therefore, it is possible to determine whether compensation is required without having to store a large number of signals.

[0044] 4 is a diagram illustrating a process 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. 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.

[0045] Therefore, the determination unit 45 uses a learning model generated in advance to convert the data signal 8. Specifically, the determination unit 45 inputs the received data signal 8 to an AE based on multiple types of learning models generated in advance.

[0046] First, the determination unit 45 inputs the received data signal 8 to an AE based on a learning model linked to the compensation factor to be determined. The learning model used here is a learning model that generates the same signal from a signal that has changed due to the linked compensation factor.

[0047] Therefore, if the input data signal is a signal that has been changed by the associated compensation factor, the characteristics required for restoration will be the same. As a result, a data signal identical to the input data signal will be output. On the other hand, if the input data signal is not a signal that has been changed by the associated compensation factor, the characteristics required for restoration will be different. As a result, a data signal different from the input data signal will be output.

[0048] Therefore, if the output signal is the same as the input signal, the judgment unit 45 judges that the compensation factor linked to the learning model used for the conversion is the compensation factor required by the received data signal.

[0049] Similarly, if the output signal differs from the input signal, the determination unit 45 determines that the compensation factor associated with the learning model used for the conversion is not the compensation factor required by the received data signal. Therefore, the determination unit 45 inputs the received data signal 8 to an AE based on a learning model associated with the undetermined compensation factor. By repeating this process, the determination unit 45 can determine the compensation factor required by the received data signal 8 from among the possible compensation factors.

[0050] Next, the determination unit 45 transmits the data signal to the compensation unit according to the determined compensation factor. Here, three types of compensation units, compensation units 46a, 46b, and 46c, are shown. The compensation units 46a, 46b, and 46c compensate for the received 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.

[0051] As described above, in the present disclosure, the required compensation factor can be determined from the received data signal, eliminating the need to use a pilot signal and improving communication speed. Furthermore, since the compensation factor is determined in advance, only the necessary compensation can be performed, reducing processing. In other words, power consumption can be reduced.

[0052] 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.

[0053] 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.

[0054] 2 Learning data 4 Teacher data 6 Data signal 8 Data signal 20 Transmitter 40 Receiver

Claims

1. A receiver that receives data 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 a plurality of types of learning by an Autoencoder (AE) based on a plurality of types of learning data; a process of storing a plurality of types of learning results; a process of determining a compensation factor required for a received data signal using a plurality of types of learning models generated based on the learning results; and a process of compensating the received data signal according to the determined compensation factor. The learning data includes teacher data corresponding to the received data and learning data corresponding to the received data, and the corresponding compensation factor is associated therewith.

2. The receiver according to claim 1, wherein the compensation factor includes "no compensation required" used when no compensation is necessary.

3. The receiver according to claim 1 or 2, wherein the compensation factor is a factor causing incompleteness of the RF circuit, and 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 comprising 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 a plurality of types of learning by an Autoencoder (AE) based on a plurality of types of learning data; a process of storing a plurality of types of learning results; a process of determining a compensation factor required for a received data signal using a plurality of types of learning models generated based on the learning results; and a process of compensating the received data signal according to the determined compensation factor. The learning data includes teacher data corresponding to the received data and learning data corresponding to the received data, and the corresponding compensation factor is associated therewith.

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