Reception device

Abstract

The present disclosure relates to a reception device. The reception device receives a data signal from a transmission device and comprises a processor and memory that stores a program that is to be executed by the processor. The processor performs processing that generates a data set that is needed to acquire a learning model that is to be used for compensation, processing that performs machine learning based on the data set and acquires a first learning model that is to be used for compensation, processing that performs clustering on a data signal such that signal points presumed to have been located at the same signal point before a signal change are in the same cluster, processing that determines signal point locations at which signal points in the same clusters are presumed to have been located before the signal change, processing that performs compensation based on the first learning model on the signal points included in the data signal assuming the locations of the signal points to be the signal point locations, and processing that demodulates the data signal that has undergone the compensation.

Description

Receiving device

[0001] The present disclosure relates to a receiving device.

[0002] In order to improve communication quality, there is a technology that outputs the original signal by performing compensation based on the signal that has changed during the communication process. For example, Non-Patent Document 1 discloses a method for improving demodulation performance by performing likelihood calculation based on the signal affected by amplifier distortion and information on the distortion constellation. The distortion constellation used in this method is identified based on the device specifications of the amplifier installed in the transmitting station.

[0003] Fukuzono, Kuriyama, Uchida, Yoshioka, and Miyagi, “Log-Likelihood Ratio for Nonlinear Distortion Constellations in High-Frequency Band Single-Carrier Transmission,” IEICE Society Conference B-5-57, August 31, 2021.

[0004] However, with the above-mentioned method, when the effect of random noise on signal changes is large, there are problems such as an increase in the time required for compensation and a decrease in compensation accuracy.

[0005] In order to solve the above-mentioned problems, an object of the present disclosure is to provide a receiving device that can reduce the time required for compensation and improve the accuracy of compensation.

[0006] An aspect of the present disclosure is preferably a receiving device that receives a data signal from a transmitting device, the receiving device including a processor and a memory that stores a program to be executed by the processor, wherein the processor is configured to perform the following processes: generating a dataset necessary to acquire a learning model to be used for compensation; performing machine learning based on the dataset to acquire a first learning model to be used for compensation; clustering the data signal so that multiple signal points that are presumed to have been located at the same signal point before a signal change are in the same cluster; determining, for multiple signal points included in the same cluster, the signal point positions that are presumed to have been located before the signal change; and, assuming that the signal points included in the data signal are located at signal point positions, performing compensation based on the first learning model on the signal points and demodulating the compensated data signal.

[0007] According to aspects of the present disclosure, it is possible to reduce the time required for compensation and at the same time improve the accuracy of compensation.

[0008] 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 receiving device according to a first embodiment of the present disclosure; FIG. 3 is a diagram illustrating changes in a data signal according to a first embodiment of the present disclosure;

[0009] 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 transmitting device 20. The transmitting device 20 is a device that transmits a data signal to a receiving device 40.

[0010] The transmitting device 20 includes a data generating unit 22. The data generating unit 22 generates a data signal for information to be transmitted to the receiving device 40, and transmits the data signal to a modulating unit .

[0011] The modulator 24 modulates the data signal and transmits it to the RF circuit unit 26. The RF circuit unit 26 performs transmission processing on the modulated data signal. The transmission processing includes, for example, converting a digitally modulated data signal into an analog signal, quadrature modulation of an I signal and a Q signal, or amplifying the data signal using an amplifier. The RF circuit unit 26 transmits the data signal that has undergone transmission processing to the receiving device 40.

[0012] The RF circuit unit 42 of the receiving device 40 performs reception processing of the transmitted data signal. The reception processing includes, for example, amplifying the data signal using an amplifier, demodulating the data signal into an I signal and a Q signal, or digitizing a digitized data signal. The RF circuit unit 42 transmits the data signal that has undergone reception processing to the clustering unit 44.

[0013] The clustering unit 44 performs clustering of the received signal. Clustering is performed, for example, so that multiple signal points that are presumed to have been located at the same signal point before the signal change are placed in the same cluster. Clustering is also performed based on the number of modulation points of the modulation method. For example, clustering may be performed for each received frame. The clustering unit 44 then transmits the data signal and cluster information to the position determination unit 45.

[0014] The above-mentioned signal change is a signal change caused by the transmission process and the reception process, such as random noise.

[0015] Clustering can be performed using machine learning such as the K-means method. Alternatively, clustering may be performed based on a second learning model obtained by machine learning using training data on the positions of signal points before and after a signal change. In this case, the training data may be a collection of multiple known data signals for one frame.

[0016] The position determination unit 45 determines the signal point positions of multiple signal points included in the same cluster to a single point. This signal point position is determined to be, for example, the signal point position that is estimated to have been located before the signal change. The signal point position may be, for example, the center of gravity of multiple signal points included in the same cluster. Alternatively, the signal point position may be, for example, the median value of multiple signal points included in the same cluster. This determination makes it possible to remove random noise associated with signal changes.

[0017] The position determination unit 45 transmits the data signal and information on the signal point positions to the compensation unit 46. The compensation unit 46 performs compensation assuming that the signal points included in the data signal are located at the transmitted signal points, and transmits the compensated data signal to the demodulation unit 48. This compensation is performed based on a first learning model acquired by machine learning performed in a preprocessing unit 60, which will be described later in detail.

[0018] The compensation described above is compensation for signal changes caused by transmission and reception processes. The cause of the signal changes is, for example, imperfections in the RF circuit. The imperfections in the RF circuit are, for example, nonlinear distortion, IQ imbalance, or phase noise of the amplifier.

[0019] The demodulation unit 48 demodulates the data signal compensated by the compensation unit 46 .

[0020] The acquisition of a model required for compensation will now be described. The wireless communication system 100 includes a preprocessing device 60. The preprocessing device 60 has a dataset generation unit 62. The dataset generation unit 62 generates a dataset required to acquire a learning model used for compensation, and transmits it to the machine learning unit 64. This dataset is a dataset that links together data before and after a signal change.

[0021] The machine learning unit 64 performs machine learning based on the data set to obtain a first learning model to be used for compensation. The machine learning unit 64 also transmits the first learning model to the compensation unit 46. As described above, the compensation unit 46 performs compensation based on the first learning model.

[0022] 2 is a diagram illustrating a hardware configuration of a receiving device according to the first embodiment of the present disclosure. Each function of the receiving device 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.

[0023] For example, the receiving device 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.

[0024] 2, the receiving device 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 receiving device 40 is also capable of inputting and outputting data to and from a computer-readable storage medium 407.

[0025] 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.

[0026] The communication unit 402 is, for example, a communication interface that communicates with a wireless device to be controlled.

[0027] The CPU 403 controls each component of the receiving device 40 and performs predetermined processing, etc. The memory 404 and HDD 405 store data, etc.

[0028] The storage medium 407 is capable of storing programs and the like that cause the receiving device 40 to execute the functions of the receiving device 40. Note that the architecture that configures the receiving device 40 is not limited to the example shown in FIG.

[0029] 3 is a diagram illustrating changes in a data signal according to the first embodiment of the present disclosure. First, the transmitting device 20 transmits a data signal 2. The data signal 2 changes due to signal changes caused by transmission processing and reception processing, and becomes a data signal 3. The signal changes are caused by, for example, random noise and imperfections in the RF circuit.

[0030] The receiving device 40 removes random noise from the data signal 3. The random noise is removed by clustering and determining the signal point positions. With the random noise removed, the data signal 3 becomes the data signal 4.

[0031] Next, the receiving device 40 compensates for the data signal 4. The data signal 4 becomes the data signal 2a through the compensation. The data signal 2a is a data signal that matches the data signal 2. The receiving device 40 then demodulates the data signal 2a.

[0032] As described above, in the present disclosure, compensation is performed after the effects of random noise are removed in advance. As a result, compensation can be performed that targets signal changes caused by factors other than random noise. This reduces the time required for compensation and improves the accuracy of compensation.

[0033] In this disclosure, the K-means method is exemplified as a clustering method, but the clustering method is not limited to this. Furthermore, the machine learning described in this disclosure is an example, and the network configuration or training data is not limited. Furthermore, the configuration of the wireless communication system is not limited.

[0034] Although machine learning has been exemplified as a compensation method in this disclosure, the present disclosure is not limited to this.Furthermore, the system configuration, modulation method, and the like are not limited.

[0035] 2 Data signal 2a Data signal 3 Data signal 4 Data signal 20 Transmitting device 40 Receiving device

Claims

1. A receiving device that receives a data signal from a transmitting device, comprising a processor and a memory that stores a program to be executed by the processor, wherein the processor is configured to perform the following processes: generating a data set necessary to acquire a learning model to be used for compensation; performing machine learning based on the data set to acquire a first learning model to be used for compensation; clustering the data signal so that multiple signal points that are presumed to have been located at the same signal point before a signal change are in the same cluster; determining, for multiple signal points included in the same cluster, the signal point positions that are presumed to have been located before the signal change; assuming that the signal point included in the data signal is located at the signal point position, performing compensation based on the first learning model on the signal point; and demodulating the compensated data signal.

2. The receiving device according to claim 1, wherein the signal point position is the center of gravity of multiple signal points included in the same cluster.

3. The receiving device according to claim 1, wherein the signal point position is the median value of multiple signal points included in the same cluster.

4. The receiving device according to claim 1, wherein the clustering is performed based on a second learning model obtained by machine learning using data on the positions of signal points before and after the signal change as training data.

Images