CN114257477A

CN114257477A - Signal detection method and related equipment

Info

Publication number: CN114257477A
Application number: CN202011011511.5A
Authority: CN
Inventors: 曾兴
Original assignee: Huawei Technologies Co Ltd
Current assignee: Huawei Technologies Co Ltd
Priority date: 2020-09-23
Filing date: 2020-09-23
Publication date: 2022-03-29
Anticipated expiration: 2040-09-23
Also published as: CN114257477B; WO2022062868A1

Abstract

The embodiment of the application provides a signal detection method and related equipment, which are used for optimizing the processing process of bit soft information estimation and improving the communication efficiency. In the method, the statistical characteristics of the signal to be detected can be processed by utilizing the neural network, and then corresponding bit soft information is output, compared with the comparison operation in the traditional max-log algorithm, the method is replaced by the more general and efficient multiply-add operation in the neural network processing process. In addition, in the method, the received signal can be randomly sampled according to the filter parameter of the received signal to obtain the sampling result of the discrete information for indicating the posterior probability of the signal to be detected, and compared with the implementation process of participating in calculation through multiple QR decompositions, the complexity of the calculation process can be reduced through the random sampling mode. Therefore, the signal processing process of the signal detection device is optimized, and the communication efficiency is improved.

Description

Signal detection method and related equipment

Technical Field

The present application relates to the field of communications, and in particular, to a signal detection method and related device.

Background

Multiple-input and multiple-output (MIMO) technology is a hot spot of current research in the field of wireless communication, and MIMO technology is adopted in various novel mobile communication systems to improve the spectrum efficiency of the systems. The MIMO technology can increase the spatial dimension of data multiplexing, so that multiple data are spatially multiplexed to the same time-frequency resource, and can also use multiple antennas to transmit the same data and/or use multiple receiving antennas to receive the same data, thereby obtaining spatial diversity gain.

In the transmission process applying the MIMO technology, a signal transmitting terminal maps bit information into a constellation symbol, modulates the constellation symbol into a signal, and transmits the signal through a transmitting antenna. Correspondingly, the signal receiving end receives the signal through the receiving antenna, demodulates the signal into the search path result of the constellation symbol, that is, demodulates the search path result to obtain the discrete information of the posterior probability of the constellation symbol, and determines the bit information in the search path result. Generally, in a signal receiving end, a process of demodulating a signal into a search path result of a constellation symbol is generally obtained through a calculation process of multiple QR operations, and a process of mapping the constellation symbol into bit information is generally obtained through an approximate calculation by a max-log algorithm.

However, in the processing procedure of implementing bit soft information estimation at the signal receiving end, the computation procedure of multiple QR operations and the computation of max-log algorithm are too complicated, and irregular searching and comparing operations exist in the computation procedure, which is not favorable for efficient implementation of a general processor and affects communication efficiency.

Disclosure of Invention

The embodiment of the application provides a signal detection method and related equipment, which are used for optimizing the processing process of bit soft information estimation and improving the communication efficiency.

A first aspect of the embodiments of the present application provides a signal detection method, which is applied to a signal detection apparatus, where the signal detection apparatus may be a network device or a terminal device, and may also be a component (e.g., a processor, a chip, or a chip system, etc.) of the network device or the terminal device. In the method, a signal detection device determines channel state information of a received signal after receiving the received signal, wherein the received signal comprises a signal to be detected; then, the device determines the filtering parameter of the received signal according to the channel state information; then, the device determines the target statistical characteristics of the signal to be detected according to the received signal and the filter parameters of the received signal, wherein the target statistical characteristics are used for indicating the statistical information of the posterior probability of the signal to be detected; and finally, the device adopts a neural network to process the target statistical characteristics at least once and then outputs target bit soft information corresponding to the signal to be detected. The signal detection device determines the statistical characteristics of the signal to be detected according to the filtering parameters of the received signal, the neural network is adopted to process the target statistical characteristics at least once and then output target bit soft information corresponding to the signal to be detected, namely the neural network is utilized to process the statistical characteristics of the signal to be detected and then output corresponding bit soft information.

In a possible implementation manner of the first aspect of the embodiment of the present application, the process of outputting the target bit soft information corresponding to the signal to be detected after performing at least one processing on the target statistical characteristic by using the neural network may specifically include: and in the neural network, mapping the target statistical characteristics and outputting bit soft information corresponding to the signal to be detected.

In this embodiment, the signal detection apparatus may perform at least one processing procedure on the target statistical characteristics of the signal to be detected in multiple ways in the neural network, and output corresponding target bit soft information, where after the target statistical characteristics of the signal to be detected are obtained, the target bit soft information of the signal to be detected is obtained through mapping processing in the neural network. Therefore, a specific implementation mode for processing and outputting the target bit soft information in the neural network is provided, and the realizability of the scheme is improved.

In a possible implementation manner of the first aspect of the embodiment of the present application, the process of outputting the target bit soft information corresponding to the signal to be detected after performing at least one processing on the target statistical characteristic by using the neural network may specifically include: in the neural network, performing fusion processing on the target statistical characteristics to obtain first statistical characteristics, wherein the first statistical characteristics are used for indicating statistical information of the target bit soft information; and outputting target bit soft information corresponding to the signal to be detected after mapping the first statistical characteristic.

In this embodiment, the signal detection apparatus may perform at least one processing process on the target statistical characteristics of the signal to be detected in multiple ways in the neural network, and output corresponding target bit soft information, where after the target statistical characteristics of the signal to be detected are obtained, after a first statistical characteristic of the statistical information for indicating the target bit soft information is obtained through fusion processing in the neural network, the first statistical characteristic is mapped, and the target bit soft information of the signal to be detected is output. Therefore, the input of the mapping processing process is optimized through the fusion processing process, a specific implementation mode for processing and outputting the target bit soft information in the neural network is provided, and the realizability of the scheme is improved.

In a possible implementation manner of the first aspect of the embodiment of the present application, outputting target bit soft information corresponding to the signal to be detected after performing at least one processing on the target statistical characteristic by using a neural network includes: in the neural network, mapping the target statistical characteristics to obtain first bit soft information corresponding to the signal to be detected; and correcting the value of the first bit soft information corresponding to the signal to be detected according to the first value range, and outputting the target bit soft information corresponding to the signal to be detected.

In this embodiment, the signal detection apparatus may perform at least one processing process on the target statistical characteristics of the signal to be detected in multiple ways in the neural network, and output corresponding target bit soft information, where after the target statistical characteristics of the signal to be detected are obtained, after first bit soft information corresponding to the signal to be detected is obtained through mapping processing in the neural network, the first bit soft information corresponding to the signal to be detected is corrected according to the first value range, and the target bit soft information of the signal to be detected is output. Therefore, the mapping result of the mapping processing process is optimized through the correction processing process, a specific implementation mode for processing and outputting the target bit soft information in the neural network is provided, and the realizability of the scheme is improved.

In a possible implementation manner of the first aspect of the embodiment of the present application, outputting target bit soft information corresponding to the signal to be detected after performing at least one processing on the target statistical characteristic by using a neural network includes: in the neural network, performing fusion processing on the target statistical characteristics to obtain second statistical characteristics, wherein the second statistical characteristics are used for indicating statistical information of second bit soft information corresponding to the signal to be detected; mapping the second statistical characteristic to obtain second bit soft information corresponding to the signal to be detected; and correcting the value of the second bit soft information corresponding to the signal to be detected according to the second value range, and outputting the target bit soft information corresponding to the signal to be detected.

In this embodiment, the signal detection apparatus may perform at least one processing process on a target statistical characteristic of a signal to be detected in multiple ways in the neural network, and output corresponding target bit soft information, where after the target statistical characteristic of the signal to be detected is obtained, a second statistical characteristic of statistical information used for indicating second bit soft information corresponding to the signal to be detected is obtained through fusion processing in the neural network, and after the second statistical characteristic is mapped to obtain the second bit soft information, a value of the second bit soft information is modified according to a second value range, and the target bit soft information of the signal to be detected is output. Therefore, the input of the mapping processing process is optimized through the fusion processing process, the mapping result of the mapping processing process is optimized through the correction processing process, a specific implementation mode for processing and outputting the target bit soft information in the neural network is provided, and the realizability of the scheme is improved.

In one possible implementation manner of the first aspect of the embodiment of the present application, the target activation function of the neural network includes a ReLu activation function and/or a softplus activation function and/or a softmax activation function.

In this embodiment, in at least one processing procedure of the neural network, the used target activation function may include a ReLu activation function and/or a softplus activation function and/or a softmax activation function, and the like. Therefore, various specific implementation modes of the activation function used in the neural network are provided, and the realizability of the scheme is improved.

In a possible implementation manner of the first aspect of the embodiment of the present application, the weight coefficient of the neural network is obtained by training a preset signal after the neural network generates a data tag through a preset algorithm.

In this embodiment, in the processing process of the neural network, the used weight coefficients are obtained by training the preset signals after the data labels are generated by the preset algorithm, and compared with the processing process in which actual transmitted real data are used as labels in the neural network and the calculated weight coefficients participate, the design of the loss function can be simplified, and meanwhile, the processing result of the neural network is optimized to approximate the optimal performance of the preset algorithm.

In a possible implementation manner of the first aspect of the embodiment of the present application, the preset algorithm may include a maximum likelihood detection MLD algorithm, or a linear minimum mean square error LMMSE algorithm.

In this embodiment, the neural network may use an MLD algorithm or an LMMSE algorithm to participate in the process of obtaining the weight coefficient during training, so that a plurality of specific implementation modes of the algorithm used by the neural network during the process of obtaining the weight coefficient during training are provided, and the realizability of the scheme is improved.

In a possible implementation manner of the first aspect of the embodiment of the present application, determining the filtering parameter of the received signal according to the channel state information includes: and determining initial filtering parameters of the received signal in a target mode according to the channel state information, wherein the target mode comprises QR decomposition, SQR decomposition processing, Cholesky decomposition, linear detection processing and the like.

In this embodiment, in the process of determining the filtering parameter of the received signal, the signal detection apparatus may use QR decomposition, SQR decomposition, Cholesky decomposition, or linear detection to participate in the calculation, so as to provide multiple specific implementation manners of the signal detection apparatus in the process of determining the filtering parameter of the received signal, and improve the realizability of the scheme.

In a possible implementation manner of the first aspect of the embodiment of the present application, the determining a filtering parameter of the received signal according to the channel state information specifically includes: the channel state information is normalized according to a first preset parameter to obtain processed channel state information; then, the received signal is normalized according to a second preset parameter to obtain a first received signal; then, determining the filtering parameter of the received signal in the target mode according to the processed channel state information; and/or, the determining the target statistical characteristic of the signal to be detected according to the received signal and the filter parameter of the received signal comprises: the received signals are normalized according to a third preset parameter to obtain second received signals; then, performing normalization processing on the filtering parameters of the received signals according to a fourth preset parameter to obtain first filtering parameters; and then, determining the target statistical characteristics of the signal to be detected according to the second received signal and the first filtering parameter.

In this embodiment, in the process of determining the initial filtering parameter of the received signal according to the channel state information, the signal detection apparatus may firstly perform normalization processing on the channel state information and the received signal respectively by using a first preset parameter and a second preset parameter in the determination process, so as to further determine the filtering parameter of the first received signal after obtaining the processed channel state information and the first received signal. In addition, in the process of determining the target statistical characteristic of the signal to be detected according to the received signal and the filter parameter of the received signal, the signal detection device may first perform model normalization processing on the received signal and the filter parameter of the received signal, respectively, to obtain a second received signal and a first filter parameter, and then further determine the target statistical characteristic of the signal to be detected. The regularization process may also be referred to as a model regularization process, and the specific implementation may include channel power normalization, value range adjustment, column permutation, and the like, or other model regularization operations. Compared with the data before the normalization processing, the data after the normalization processing is more beneficial to the value area (such as continuous integer value) of the subsequent processing and the better sequencing order.

In a possible implementation manner of the first aspect of the embodiment of the present application, the process of determining the target statistical characteristic of the signal to be detected according to the received signal and the filter parameter of the received signal may specifically include: randomly sampling the received signal according to the filtering parameter of the received signal to obtain a sampling result, wherein the sampling result is used for indicating discrete information of the posterior probability of the signal to be detected; and calculating the target statistical characteristics of the signal to be detected according to the sampling result.

In this embodiment, the signal detection device may perform random sampling on the received signal according to the filtering parameter of the received signal to obtain a sampling result of the discrete information indicating the posterior probability of the signal to be detected, and then calculate the target statistical characteristic of the signal to be detected according to the sampling result, that is, the target statistical characteristic of the signal to be detected is determined through a random sampling process. Therefore, a specific implementation mode for determining the target statistical characteristics of the signal to be detected according to the received signal and the filtering parameter of the received signal is provided, and the realizability of the scheme is improved.

In a possible implementation manner of the first aspect of the embodiment of the present application, the randomly sampling the received signal according to the filtering parameter of the received signal, and obtaining a sampling result specifically may include: and randomly sampling the received signal according to the filtering parameters of the received signal and a target sampling structure to obtain a sampling result, wherein the target sampling structure comprises a serial sampling structure and/or a parallel sampling structure.

In this embodiment, the signal detection apparatus may perform random sampling on the received signal through the serial sampling structure and/or the parallel sampling structure to obtain a sampling result. Therefore, a specific implementation mode of the random sampling process is provided, and the realizability of the scheme is improved.

A second aspect of the embodiments of the present application provides a signal detection method, which is applied to a signal detection apparatus, where the signal detection apparatus may be a network device or a terminal device, and may also be a component (e.g., a processor, a chip or a chip system, etc.) of the network device or the terminal device. In the method, a signal detection device determines channel state information of a received signal after receiving the received signal, wherein the received signal comprises a signal to be detected; determining a filtering parameter of the received signal according to the channel state information; randomly sampling the received signal according to the filtering parameter of the received signal to obtain a sampling result, wherein the sampling result is used for indicating discrete information of the posterior probability of the signal to be detected; and obtaining target bit soft information corresponding to the signal to be detected according to the sampling result. The signal detection device carries out random sampling on the received signal according to the filtering parameters of the received signal to obtain the sampling result of the discrete information used for indicating the posterior probability of the signal to be detected.

In a possible implementation manner of the second aspect of the embodiment of the present application, determining the filtering parameter of the received signal according to the channel state information includes: and determining initial filtering parameters of the received signal in a target mode according to the channel state information, wherein the target mode comprises QR decomposition, SQR decomposition processing, Cholesky decomposition, linear detection processing and the like.

In a possible implementation manner of the second aspect of the embodiment of the present application, the determining the filtering parameter of the received signal according to the channel state information specifically includes: the channel state information is normalized according to a first preset parameter to obtain processed channel state information; then, the received signal is normalized according to a second preset parameter to obtain a first received signal; then, determining the filtering parameter of the received signal in the target mode according to the processed channel state information; and/or, the determining the target statistical characteristic of the signal to be detected according to the received signal and the filter parameter of the received signal comprises: the received signal is normalized according to a third preset parameter to obtain a second received signal; then, the filtering parameters of the received signal are normalized according to a fourth preset parameter to obtain a first filtering parameter; and then, determining the target statistical characteristics of the signal to be detected according to the second received signal and the first filtering parameter.

In this embodiment, in the process of determining the initial filtering parameter of the received signal according to the channel state information, the signal detection apparatus may use the first preset parameter and the second preset parameter to respectively perform normalization processing on the channel state information and the received signal in the determination process, so as to further determine the filtering parameter of the first received signal after obtaining the processed channel state information and the first received signal. In addition, in the process of determining the target statistical characteristic of the signal to be detected according to the received signal and the filter parameter of the received signal, the signal detection device may first perform model normalization processing on the received signal and the filter parameter of the received signal, respectively, to obtain a second received signal and a first filter parameter, and then further determine the target statistical characteristic of the signal to be detected. The regularization process may also be referred to as a model regularization process, and the specific implementation may include channel power normalization, value range adjustment, column permutation, and the like, or other model regularization operations. Compared with the data before the normalization processing, the data after the normalization processing is more beneficial to the value area (such as continuous integer value) of the subsequent processing and the better sequencing order.

In a possible implementation manner of the second aspect of the embodiment of the present application, the randomly sampling the received signal according to the filtering parameter of the received signal, and obtaining the sampling result specifically includes: and randomly sampling the received signal according to the filtering parameters of the received signal and a target sampling structure to obtain a sampling result, wherein the target sampling structure comprises a serial sampling structure and/or a parallel sampling structure.

A third aspect of the embodiments of the present application provides a signal detection apparatus, where the signal detection apparatus may be a network device or a terminal device, or may be a component (e.g., a processor, a chip, or a system-on-chip) of the network device or the terminal device. The device comprises a determining unit and a processing unit: the determining unit is used for determining the channel state information of a received signal, wherein the received signal comprises a signal to be detected; the determining unit is further configured to determine a filtering parameter of the received signal according to the channel state information; the determining unit is further configured to determine a target statistical characteristic of the signal to be detected according to the received signal and the filter parameter of the received signal, where the target statistical characteristic is used to indicate statistical information of a posterior probability of the signal to be detected; the processing unit is used for outputting target bit soft information corresponding to the signal to be detected after the target statistical characteristics are processed at least once by adopting a neural network. The processing unit in the signal detection device determines the statistical characteristics of the signal to be detected according to the filtering parameters of the received signal, the neural network is adopted to process the target statistical characteristics at least once and then output target bit soft information corresponding to the signal to be detected, namely the neural network is utilized to process the statistical characteristics of the signal to be detected and then output corresponding bit soft information.

In a possible implementation manner of the third aspect of the embodiment of the present application, the processing unit is specifically configured to:

and in the neural network, mapping the target statistical characteristics and outputting bit soft information corresponding to the signal to be detected.

in the neural network, performing fusion processing on the target statistical characteristics to obtain first statistical characteristics, wherein the first statistical characteristics are used for indicating statistical information of the target bit soft information;

and outputting target bit soft information corresponding to the signal to be detected after mapping the first statistical characteristic.

in the neural network, mapping the target statistical characteristics to obtain first bit soft information corresponding to the signal to be detected;

and correcting the value of the first bit soft information corresponding to the signal to be detected according to the first value range, and outputting the target bit soft information corresponding to the signal to be detected.

in the neural network, performing fusion processing on the target statistical characteristics to obtain second statistical characteristics, wherein the second statistical characteristics are used for indicating statistical information of second bit soft information corresponding to the signal to be detected;

mapping the second statistical characteristic to obtain second bit soft information corresponding to the signal to be detected;

and correcting the value of the second bit soft information corresponding to the signal to be detected according to the second value range, and outputting the target bit soft information corresponding to the signal to be detected.

In one possible implementation manner of the third aspect of the embodiment of the present application, the target activation function of the neural network includes a ReLu activation function and/or a softplus activation function and/or a softmax activation function.

In a possible implementation manner of the third aspect of the embodiment of the present application, the weight coefficient of the neural network is obtained by training a preset signal after the neural network generates a data tag through a preset algorithm.

In a possible implementation manner of the third aspect of the embodiment of the present application, the preset algorithm includes a maximum likelihood detection MLD algorithm, or a linear minimum mean square error LMMSE algorithm.

In a possible implementation manner of the third aspect of the embodiment of the present application, the determining unit is specifically configured to:

and determining the filtering parameters of the received signal in a target mode according to the channel state information, wherein the target mode comprises Cholesky decomposition processing, QR decomposition processing, SQR decomposition processing or linear detection processing.

In a possible implementation manner of the third aspect of the embodiment of the present application, the determining unit is specifically configured to: the channel state information is normalized according to a first preset parameter to obtain processed channel state information; the received signal is normalized according to a second preset parameter to obtain a first received signal; determining a filtering parameter of the first received signal in the target mode according to the processed channel state information;

and/or the presence of a gas in the gas,

the received signal is normalized according to a third preset parameter to obtain a second received signal;

the filtering parameters of the received signal are normalized according to a fourth preset parameter to obtain first filtering parameters;

and determining the target statistical characteristics of the signal to be detected according to the second received signal and the first filtering parameter.

randomly sampling the received signal according to the filtering parameter of the received signal to obtain a sampling result, wherein the sampling result is used for indicating discrete information of the posterior probability of the signal to be detected;

and calculating the target statistical characteristics of the signal to be detected according to the sampling result.

and randomly sampling the received signal according to the filtering parameters of the received signal and a target sampling structure to obtain a sampling result, wherein the target sampling structure comprises a serial sampling structure and/or a parallel sampling structure.

In the third aspect of the embodiment of the present application, the constituent modules of the signal detection apparatus may also be configured to execute the steps executed in each possible implementation manner of the first aspect, which may specifically refer to the first aspect, and are not described here again.

A fourth aspect of the embodiments of the present application provides a signal detection apparatus, including a determination unit and a processing unit:

the determining unit is used for determining the channel state information of a received signal, wherein the received signal comprises a signal to be detected;

the determining unit is further configured to determine a filtering parameter of the received signal according to the channel state information;

the determining unit is further configured to perform random sampling on the received signal according to the filtering parameter of the received signal to obtain a sampling result, where the sampling result is used to indicate discrete information of a posterior probability of the signal to be detected;

and the processing unit is used for obtaining target bit soft information corresponding to the signal to be detected according to the sampling result.

In a possible implementation manner of the fourth aspect of the embodiment of the present application, the determining unit is specifically configured to:

the channel state information is normalized according to a first preset parameter to obtain processed channel state information;

the received signal is normalized according to a second preset parameter to obtain a first received signal;

determining a filtering parameter of the first received signal in the target mode according to the processed channel state information;

and/or the presence of a gas in the gas,

and randomly sampling the received signal according to the filtering parameters of the received signal and a target sampling structure to obtain the sampling result, wherein the target sampling structure comprises a serial sampling structure and/or a parallel sampling structure.

In the fourth aspect of the embodiment of the present application, the constituent modules of the signal detection apparatus may also be configured to execute the steps executed in each possible implementation manner of the second aspect, and reference may be specifically made to the second aspect, which is not described herein again.

A fifth aspect of embodiments of the present application provides a communication device, where the communication device includes a processor and a communication interface, the communication interface is coupled to the processor, and the processor is configured to execute a computer program or instructions to perform the method according to the first aspect or any one of the possible implementations of the first aspect, or to perform the method according to the second aspect or any one of the possible implementations of the second aspect.

A sixth aspect of embodiments of the present application provides a computer-readable storage medium storing one or more computer-executable instructions, which, when executed by a processor, perform a method according to the first aspect or any one of the possible implementations of the first aspect; alternatively, when the computer executable instructions are executed by a processor, the processor performs the method according to the second aspect or any one of the possible implementations of the second aspect.

A seventh aspect of the embodiments of the present application provides a computer program product (or computer program) storing one or more computers, where when the computer program product is executed by a processor, the processor executes the method of the first aspect or any one of the possible implementation manners of the first aspect; alternatively, the computer program product, when executed by the processor, performs the method of the second aspect or any one of the possible implementations of the second aspect.

An eighth aspect of the present embodiment provides a chip system, where the chip system includes a processor, configured to support a network device to implement the functions related to the first aspect or any one of the possible implementation manners of the first aspect; or for supporting a network device to implement the functionality referred to in the second aspect or any one of the possible implementations of the second aspect. In one possible design, the system-on-chip may further include a memory, which stores program instructions and data necessary for the network device. The chip system may be constituted by a chip, or may include a chip and other discrete devices.

A ninth aspect of embodiments of the present application provides a communication system, where the communication system includes at least the communication apparatus of the third aspect, or the communication system includes at least the communication apparatus of the fourth aspect, or the communication system includes at least the communication apparatus of the fifth aspect.

According to the technical scheme, the embodiment of the application has the following advantages: the signal detection device can determine the statistical characteristics of the signal to be detected according to the filtering parameters of the received signal, the neural network is adopted to process the target statistical characteristics at least once and then output the target bit soft information corresponding to the signal to be detected, namely the neural network is utilized to process the statistical characteristics of the signal to be detected and then output the corresponding bit soft information, compared with the comparison operation in the traditional max-log algorithm, the method has the advantages that the more general and efficient multiplication and addition operation in the neural network processing process is replaced, and the signal processing process of the signal detection device is optimized. In addition, the signal detection device can also perform random sampling on the received signal according to the filtering parameter of the received signal to obtain a sampling result of discrete information for indicating the posterior probability of the signal to be detected.

Drawings

Fig. 1 is a schematic diagram of a communication system in an embodiment of the present application;

FIG. 2 is a schematic diagram of a signal processing process implemented by the signal detection device using a QR algorithm;

fig. 3 is a schematic diagram of a signal processing method according to an embodiment of the present application;

fig. 4-1 is another schematic diagram of a signal processing method according to an embodiment of the present disclosure;

fig. 4-2 is another schematic diagram of a signal processing method according to an embodiment of the present disclosure;

fig. 4-3 are another schematic diagrams of a signal processing method according to an embodiment of the present disclosure;

fig. 5 is another schematic diagram of a signal processing method according to an embodiment of the present disclosure;

fig. 6 is a schematic diagram of a signal processing apparatus according to an embodiment of the present application;

fig. 7 is another schematic diagram of a signal processing apparatus according to an embodiment of the present disclosure.

Detailed Description

The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

First, some terms in the embodiments of the present application are explained so as to be easily understood by those skilled in the art.

1. The terminal equipment: may be a wireless terminal device capable of receiving network device scheduling and indication information, which may be a device providing voice and/or data connectivity to a user, or a handheld device having wireless connection capability, or other processing device connected to a wireless modem.

The terminal devices, which may be mobile terminal devices such as mobile telephones (or "cellular" telephones), computers, and data cards, for example, mobile devices that may be portable, pocket, hand-held, computer-included, or vehicle-mounted, may communicate with one or more core networks or the internet via a Radio Access Network (RAN). Examples of such devices include Personal Communication Service (PCS) phones, cordless phones, Session Initiation Protocol (SIP) phones, Wireless Local Loop (WLL) stations, Personal Digital Assistants (PDAs), tablet computers (pads), and computers with wireless transceiving functions. A wireless terminal device may also be referred to as a system, a subscriber unit (subscriber unit), a subscriber station (subscriber station), a mobile station (mobile station), a Mobile Station (MS), a remote station (remote station), an Access Point (AP), a remote terminal device (remote terminal), an access terminal device (access terminal), a user terminal device (user terminal), a user agent (user agent), a Subscriber Station (SS), a user terminal device (CPE), a terminal (terminal), a User Equipment (UE), a Mobile Terminal (MT), etc. The terminal device may also be a wearable device and a next generation communication system, for example, a terminal device in a 5G communication system or a terminal device in a Public Land Mobile Network (PLMN) for future evolution, etc.

2. A network device: may be a device in a wireless network, for example, a network device may be a Radio Access Network (RAN) node (or device) that accesses a terminal device to the wireless network, which may also be referred to as a base station. Currently, some examples of RAN equipment are: a new generation base station (gbodeb), a Transmission Reception Point (TRP), an evolved Node B (eNB), a Radio Network Controller (RNC), a Node B (NB), a Base Station Controller (BSC), a Base Transceiver Station (BTS), a home base station (e.g., a home evolved Node B or a home Node B, HNB), a Base Band Unit (BBU), or a wireless fidelity (Wi-Fi) Access Point (AP) in a 5G communication system. In addition, in one network configuration, the network device may include a Centralized Unit (CU) node, or a Distributed Unit (DU) node, or a RAN device including a CU node and a DU node.

The network device can send configuration information (for example, carried in a scheduling message and/or an indication message) to the terminal device, and the terminal device further performs network configuration according to the configuration information, so that network configuration between the network device and the terminal device is aligned; or, the network configuration between the network device and the terminal device is aligned through the network configuration preset in the network device and the network configuration preset in the terminal device. In particular, "alignment" refers to the fact that when an interactive message exists between a network device and a terminal device, the two devices are consistent in understanding the carrier frequency of interactive messaging, the determination of the type of interactive message, the meaning of the field information carried in the interactive message, or other configurations of the interactive message.

Furthermore, the network device may be other means for providing wireless communication functionality for the terminal device, where possible. The embodiments of the present application do not limit the specific technologies and the specific device forms used by the network devices. For convenience of description, the embodiments of the present application are not limited.

The network device may also include a core network device including, for example, an access and mobility management function (AMF), a User Plane Function (UPF), a Session Management Function (SMF), or the like.

In the embodiment of the present application, the apparatus for implementing the function of the network device may be a network device, or may be an apparatus capable of supporting the network device to implement the function, for example, a system on chip, and the apparatus may be installed in the network device. In the technical solution provided in the embodiment of the present application, a device for implementing a function of a network device is taken as an example of a network device, and the technical solution provided in the embodiment of the present application is described.

3. A neural network: the neural network may be composed of neural units, the neural units may refer to operation units with xs and intercept 1 as inputs, and the output of the operation units may be:

where s is 1,2, … … n, n is a natural number greater than 1, Ws is the weight of xs, and b is the bias of the neural unit. f is an activation function (activation functions) of the neural unit for introducing a nonlinear characteristic into the neural network to convert an input signal in the neural unit into an output signal. The output signal of the activation function may be used as an input to the next convolutional layer. The activation function may be a sigmoid function. A neural network is a network formed by a number of the above-mentioned single neural units joined together, i.e. the output of one neural unit may be the input of another neural unit. The input of each neural unit can be connected with the local receiving domain of the previous layer to extract the characteristics of the local receiving domain, and the local receiving domain can be a region composed of a plurality of neural units. A neural network may include "layers" of multiple neural units, such as an input layer, a hidden layer, and an output layer. The input layer is responsible for receiving input data and distributing to the hidden layer these hidden layers are responsible for required calculation and output result and give the output layer, and then export the output result by the output layer.

4. The terms "system" and "network" in the embodiments of the present application may be used interchangeably. "at least one" means one or more, "a plurality" means two or more. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a alone, A and B together, and B alone, wherein A and B may be singular or plural. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. "at least one of the following" or similar expressions refer to any combination of these items, including any combination of the singular or plural items. For example, "at least one of A, B, and C" includes A, B, C, AB, AC, BC, or ABC. And, unless specifically stated otherwise, the embodiments of the present application refer to the ordinal numbers "first", "second", etc., for distinguishing between a plurality of objects, and do not limit the order, sequence, priority, or importance of the plurality of objects.

Fig. 1 is a schematic diagram of a communication system according to the present application. In the communication system shown in fig. 1, at least one network device and at least one terminal device are included, for example, the network device 101, the terminal device 102, the terminal device 103, the terminal device 104, the terminal device 105, the terminal device 106, the terminal device 107, and the like shown in fig. 1. In the example shown in fig. 1, the terminal device 102 is a vehicle, the terminal device 103 is an intelligent air conditioner, the terminal device 104 is an intelligent fuel dispenser, the terminal device 105 is a mobile phone, the terminal device 106 is an intelligent cup, and the terminal device 107 is a printer.

In the communication process of the communication system shown in fig. 1, the signal transmitting end may be a network device and/or a terminal device, the signal receiving end may also be a network device and/or a terminal device, and a multiple-input and multiple-output (MIMO) technology may be applied to a signal transmission process between the signal transmitting end and the signal receiving end. The MIMO technology is a hot spot of current research in the field of wireless communication, and is adopted in various novel mobile communication systems to improve the spectrum efficiency of the systems. The MIMO technology can increase the spatial dimension of data multiplexing, so that multiple data are spatially multiplexed to the same time-frequency resource, and can also use multiple antennas to transmit the same data and/or use multiple receiving antennas to receive the same data, thereby obtaining spatial diversity gain.

In the transmission process applying the MIMO technology, a signal transmitting terminal maps bit information into a constellation symbol, modulates the constellation symbol into a signal, and transmits the signal through a transmitting antenna. Correspondingly, the signal receiving end receives the signal through the receiving antenna, demodulates the signal into the sending probability of the constellation symbol, determines bit soft information according to the probability result, and finally performs decoding according to the bit soft information, for example, through a search path result, and determines the bit information in the search path result. In the signal transmitting end, the baseband processing usually includes two important processes of channel coding and modulation, and correspondingly, in the signal receiving end, the baseband processing includes demodulation and channel decoding processes. The processing procedures of the signal transmitting end and the signal receiving end will be described below.

In a signal transmitting end, a modulation process maps coded bit information (taking a value of 0 or 1) into constellation symbols, taking 16QAM modulation in Quadrature Amplitude Modulation (QAM) modulation as an example, that is, every 4 bits (corresponding to 2^4 ^ 16 states) of information are mapped into a constellation symbol of a two-dimensional (generally called as an I/Q path) plane, and a fixed mapping relationship exists between each bit combination state and each modulation symbol, such as gray code (gray code) mapping. Then, the transmitting end performs carrier modulation on the constellation symbols and transmits the constellation symbols, wherein the carrier modulation may be implemented by Orthogonal Frequency Division Multiplexing (OFDM).

In a signal receiving end, the signal receiving end needs to calculate bit soft information, that is, a probability that each information bit takes a value of 0 or a value of 1 is obtained according to a fixed mapping relationship between a bit state and a modulation symbol, and the process can be expressed by a formula (1):

in the formula (1), P_s(i) Denotes the demodulation probability, P, for the ith symbol_b(j) Indicating the probability corresponding to the jth bit.

Thereafter, the decoder in the signal receiving end compares the logarithmic ratio of the bit soft information (i.e., the bit soft information)

And/or the presence of a gas in the gas,

) And decoding is realized. Obviously, in the formula (1), summing the probabilities of all constellation symbols corresponding to the jth bit value being 0 and the jth bit value being 1 can obtain: p_b(b_j＝0)+P_b(b_j＝1)＝1。

In addition, due to the influence of noise, interference, etc. in the actual communication system and the wide application of the MIMO technology, before constellation demodulation, i.e. before the process of calculating formula (1), it is necessary to obtain a constellation probability estimation result by using the MIMO detection technology, where the constellation probability estimation result may be denoted as P_s(i) In that respect Specifically, under the assumption of gaussian noise and ideal channel information, Maximum Likelihood Detection (MLD) is an optimal detection algorithm, but the complexity increases exponentially with the increase of the modulation mode and the number of MIMO multiplexed streams, and an actual system cannot be realized. The algorithm which can be realized practically can be divided into a linear detection algorithm and a nonlinear detection algorithm, the nonlinear detection algorithm can be called as a suboptimal MLD algorithm, and the performance of the nonlinear detection algorithm approaches to the optimal MLD algorithm. The linear detection algorithm is low in implementation complexity, but the performance is higher than that of the optimal MLD algorithm in loss; the performance of the nonlinear detection algorithm approaches that of the optimal MLD algorithm, but the implementation complexity is high, and it is not easy to realize with a general-purpose processor, such as a Graphics Processing Unit (GPU), a Digital Signal Processor (DSP), and the like.

For the signal receiving end, the received signal model can be expressed by formula (2):

y＝Hs+n (2)

in equation (2), y represents a received signal vector, H represents a channel matrix of the MIMO system, s represents a transmission constellation symbol vector, and n represents independent additive zero-mean complex gaussian noise.

In an actual communication system, n is an unknown random noise, y is a received signal, and the received signal y at least includes a pilot symbol (s known) and a data symbol (s unknown). For the signal receiving end, H (called channel estimate) can be estimated from the pilot symbols (known as y and s), and then this channel estimation result is used for the data symbols together for the estimation of s.

Based on the received signal model shown in formula (2), performing suboptimal MLD soft detection process using a nonlinear detection algorithm can be approximately represented by formula (3):

in the formula (3), | y-Hs | Y calculation²The value of the metric is represented and,

represents all possible sets of constellation symbols s, { s₁,s₂,…,s_KAnd represents k constellation combination paths corresponding to the constellation symbol s when the metric value is minimum.

Further, considering the effect of gaussian additive noise, in order to solve equation (1), the max-log algorithm can be generally adopted to approximate and calculate bit soft information, that is, bit-by-bit from the set { s }₁,s₂,…,s_KThe minimum metric value is selected as output and recorded as

Namely, expressed by formula (4):

thereafter, the result of taking 0 and 1 for each bit is subtracted to obtain the final bit log-likelihood ratio (LLR), i.e. LLR

Or

In order to solve formula (3), a classical multipath parallel search algorithm may be used, and an implementation block diagram of the overall scheme is shown in fig. 2, which specifically includes:

step 1, performing column permutation on the channel matrix H, thereby obtaining channel matrix results of different column sorting combinations, and recording the results as: { H₁,H₂,…,H_MIn which H_m＝HP_m,m＝1,2,…,M，P_mIs a column permutation matrix.

Step 2, sequentially carrying out orthogonal triangle (QR) decomposition on all sequenced channel matrixes obtained in the step 1, namely H exists_m＝Q_mR_mAnd M is 1,2, …, M. Here Q_mAs a unitary matrix, i.e.

R_mIs an upper triangular matrix.

Step 3, using the matrix

Filtering the received signal y and the channel matrix H to obtain an equivalent received signal model, namely:

step 4, utilizing R_mThe triangular structure of the matrix can realize the search of the constellation points and the calculation of corresponding metric values by a way of solving equations in series. In particular, for the sake of clarity of presentation, it is noted

) The following can be obtained:

wherein, the last Nth layer can be obtained

Then according to the modulation mode, from the constellation set

Selecting a distance

The nearest P constellations are used as candidate constellations, then the constellation values are brought into the equation formed by the previous layers for elimination, and the equation solution is hard judged to be a constellation set

And the nearest constellation point in the previous layer is brought into the previous layer, so as to realize multi-path parallel interference cancellation (SIC). And finally, calculating the metric value corresponding to each group of search results, wherein the metric value corresponding to the ith group is calculated by the following expression:

η_i＝||y-Hs_i||²,i＝1,2,…,M×P，

wherein s is_iFor the ith set of constellation search results, η_iA metric value corresponding to the set of search results.

And (3) executing the steps 2 to 4 on each column replacement result in the step 1, obtaining M multiplied by P constellation path search results and metric values thereof in total, and finally solving the problem of the formula (1) approximately according to the solving method of the formula (4) so as to obtain bit log-likelihood ratio (LLR) values.

As can be seen from the above, the signal receiving end has at least the following problems in the process of implementing bit soft information estimation: when LLR calculation is carried out, namely in the solving process of the formula (4), the max-log algorithm is adopted to select from the constellation path searching results bit by bit, which are all non-regular searching and comparing operations and are not beneficial to the efficient realization of a general processor; in addition, in the nonlinear detection process, that is, in the solving process of the formula (3), multiple QR decompositions need to be performed, so that the algorithm implementation complexity is high, and the communication efficiency is affected.

In order to solve the problem that the efficiency of the processing procedure for implementing bit soft information estimation is low, embodiments of the present application provide various solutions, which can be implemented separately from different aspects of solving the problem, and will be described in detail below.

Fig. 3 is a schematic diagram of a signal detection method in an embodiment of the present application, and as shown in fig. 3, the signal detection method includes the following steps.

S101, determining channel state information of a received signal;

in this embodiment, after receiving a received signal, a signal detection apparatus determines channel state information of the received signal, where the received signal includes a signal to be detected.

Specifically, the signal detection device may be a network device or a terminal device, and may also be a component (e.g., a processor, a chip or a system-on-chip, etc.) of the network device or the terminal device.

In step S101, the signal detection apparatus may obtain a received signal containing a signal to be detected through wired communication or wireless communication, and determine channel state information of the received signal. For the signal detection device, the received signal includes signals to be detected of a plurality of transmitting terminals, and the signals to be detected of each transmitting terminal are detected through the processes of steps S101 to S104 in this embodiment, so as to obtain soft bit information corresponding to each signal to be detected.

For example, in step S101, the signal detection apparatus may determine the channel state information of the received signal through the model shown in formula (2), that is, determine the channel matrix H used for representing the MIMO system in formula (2) as the channel state information of the received signal.

S102, determining a filtering parameter of the received signal according to the channel state information;

in this embodiment, the signal detection apparatus may further determine the filtering parameter of the received signal according to the channel state information determined in step S101.

In a possible implementation manner, in the implementation process of step S102, the signal detection apparatus may determine the filtering parameter of the received signal in a target manner according to the channel state information, where the target manner may include Cholesky decomposition processing, QR decomposition processing, sequential QR decomposition (SQR decomposition) processing, or linear detection processing, or other processing manners, which are not limited herein. The following will be described by way of specific examples:

the method a and the process of determining the filtering parameter of the received signal in step S102 may be obtained through Cholesky decomposition, and specifically include the following steps:

step a1, calculating a channel correlation matrix according to the channel state information, that is:

R_hh＝H^HH+I；

wherein R is_hhIs a channel correlation matrix, H^HA transposed conjugate matrix of H, I is used to indicate noise covariance information.

Specifically, when the variance of complex gaussian noise is 1, I indicating noise covariance information is an identity matrix. More generally, when the noise is colored noise caused by interference or the like, the noise covariance information may be in other forms, but may be equivalently transformed into an identity matrix by a whitening operation. Illustratively, the whitening operation refers to, for a general noise covariance matrix R_uuIt can be decomposed Cholesky to get R_uu＝LL^HThen, the decomposition result is inverted to obtain a whitening matrix L^-1And multiplying the whitening matrix by the received signal and the channel matrix H to obtain a noise covariance matrix of the equivalent model as an identity matrix I.

Step A2, performing Cholesky decomposition on the channel correlation matrix to obtain:

wherein L is_hIs a lower triangular matrix, and the lower triangular matrix,

is L_hThe transposed conjugate matrix of (2).

Step A4, calculating the lower triangular matrix L_hThe inverse of (c), noted:

and use

Filtering the received signal to obtain:

or

Wherein y is used for indicating the received signal before filtering or the equivalent signal after model normalization,

for indicating the filtered signal. The filter parameters include L_h、

H^HAnd

H^Hor other forms derived from such transformations, are not intended to be limiting herein.

In the mode B, the process of determining the filtering parameter of the received signal in step S102 may be obtained by QR decomposition, and specifically includes the following steps:

step B1, QR decomposition or SQR decomposition is carried out according to the channel state information to obtain:

wherein I is used to indicate noise covariance information; q, R is used for indicating the unitary matrix and the upper triangular matrix obtained after decomposition; the form of the decomposition result obtained by the SQR decomposition is completely consistent with that of the QR decomposition, except that the SQR decomposition has one more matrix P related to column permutation.

Step B2, according to the result of QR decomposition, can obtain:

where y is used to indicate the received signal before filtering or the equivalent signal after model warping,

for indicating the filtered signal. The filter parameters include Q^HAnd R, and partial elements transformed thereby or extracted therefrom, e.g. block matrix extraction, i.e. from

Extracting Q₁、Q₁、Q₂、Q₃、Q₄As well as other forms derived from such transformations, as filter parameters.

In the mode C, the process of determining the filtering parameter of the received signal in step S102 may be obtained by processing through a linear detection method, for example, a Linear Minimum Mean Square Error (LMMSE) algorithm, a maximum ratio combining detection algorithm, a zero forcing detection algorithm, and the like. A Linear Minimum Mean Square Error (LMMSE) algorithm is described as an example of the linear detection method. By means of the LMMSE algorithm:

step C1: filtering the received signal or the equivalent signal after the model is structured by using the filtering parameters corresponding to the LMMSE, namely:

where I is used to indicate noise covariance information, (H)^HH+I)^-1H^HThe filter parameters, or other forms derived from the transformation, are not limited herein.

Optionally, in the implementation process of step S102, the optimization may be further performed according to an introduction of a regularization process, where the regularization process may also be referred to as a model regularization process, and the specific implementation may include channel power de-normalization, value range adjustment, sequence permutation, and the like, or other model regularization operations. Compared with the data before the normalization processing, the data after the normalization processing is more beneficial to the value area (such as continuous integer value) of the subsequent processing and the better sequencing order.

Specifically, in the implementation process of step S102, the signal detection apparatus may further determine the filter parameter of the received signal after performing the model normalization process on the channel state information in the model normalization process. The signal detection device normalizes the channel state information according to a first preset parameter to obtain processed channel state information, and normalizes the received signal according to a second preset parameter to obtain a first received signal; then, step a1, step a2 and step A3 in the above-mentioned mode a are executed again, or step B1 and step B2 in the above-mentioned mode B are executed again, or step C1 in the above-mentioned mode C is executed again, that is, the filtering parameters of the first received signal are determined according to the processed channel state information. The implementation of the first preset parameter and the second preset parameter may be a preset numeric value range, a preset vector, a preset matrix, or other parameters, and is not limited herein.

Illustratively, in the implementation process of the model normalization in step S102, the implementation process specifically includes the following steps:

firstly, completing model normalization operation according to a modulation mode of a signal to be detected, and aiming at converting the signal to be detected into a value area (such as continuous integer value) which is more beneficial to subsequent processing and obtaining a better sequencing sequence. Taking a QAM modulation transmission scheme commonly used in a wireless communication system, which is a Long Term Evolution (LTE) system or a New Radio (NR) system, as an example, channel state information can be transformed into:

accordingly, the received signal may be further transformed into:

wherein e represents a column vector with all elements being 1, and the dimension of the column vector is the same as the column number of H; the gamma vector is a power normalization factor determined by the modulation mode of the signal to be detected, and values are shown in the following table 1 according to different modulation modes; diag { γ } represents a diagonal matrix composed of vector γ (i.e., the first preset parameter is realized by 2diag { γ } in the above formula, and the second preset parameter is realized by Hdiag { γ } e in the above formula).

Optionally, the matrix may be further aligned

And carrying out proper column replacement operation to realize the reordering of each stream. Permutation order or column permutation matrix P may pass through pairs

The SQRD operation can be preset and configured in other modes. Then the reordered equivalent channel is

In addition, as an optional implementation manner, the model normalization operation may be processed in the processing process of this embodiment and the subsequent embodiments by using the model normalization operation

Or

As channel state information,

The received signal can participate in the calculation without model regulating operationH is used as the channel state information, y is used as the received signal to participate in the calculation, and is not limited herein.

TABLE 1

S103, determining target statistical characteristics of the signal to be detected according to the received signal and the filtering parameters of the received signal;

in this embodiment, the signal detection device determines the target statistical characteristic of the signal to be detected according to the received signal and the filter parameter of the received signal obtained in step S102, where the target statistical characteristic is used to indicate statistical information of the posterior probability of the signal to be detected.

In a possible implementation manner, the optimization may also be further performed according to a process of introducing a warping process, where the warping process may also be referred to as a model warping process, and the specific implementation may include channel power de-normalization, value range adjustment, column permutation, and the like, or other model warping operations. Compared with the data before the normalization processing, the data after the normalization processing is more beneficial to the value area (such as continuous integer value) of the subsequent processing and the better sequencing order.

Specifically, in the implementation process of step S103, in the process of the signal detection apparatus, after performing model normalization on the received signal and the filter parameter of the received signal, and obtaining the second received signal and the first filter parameter, the signal detection apparatus further determines the target statistical characteristic of the signal to be detected. The third preset parameter and the fourth preset parameter may be a preset value range, a preset vector, a preset matrix, or other parameters, and are not limited herein.

Illustratively, the filtering parameter of the received signal obtained by the mode a, i.e. Cholesky decomposition in step S102 is as

Will be described by way of example only,in the implementation of the model warping in step S103, the received signal may be transformed into:

the filter parameters of the received signal may be transformed into:

wherein the content of the first and second substances,

rho is a matrix

A diagonal matrix composed of the main diagonal elements of (a); gamma is a diagonal matrix formed by power normalization factors generated by constellation modulation of the signal to be detected, and the corresponding power normalization factors under different modulation modes are realized as shown in the table 1.

As a filter parameter of the received signal,

The received signal can be used for calculation without model regulation operation

(or other forms of the foregoing forms a, B, and C) participate in the calculation as the filter parameter of the received signal, and y is the received signal, which is not limited herein.

In a possible implementation manner, in step S103, the target statistical characteristic of the signal to be detected may be determined through a linear processing process, and specifically, the linear processing process may be implemented through an LMMSE algorithm, a maximum ratio combining detection algorithm, a zero forcing detection algorithm, and the like, which is not limited herein.

Taking the implementation process of the LMMSE algorithm in step S102 (i.e., the method C) as an example, the method C may determine that the filter parameter of the received signal is (H)^HH+I)^-1H^H(ii) a Result of filtering

Is the corresponding first moment statistical feature, (H)^HH+I)^- ¹H^HThe main diagonal elements of the matrix shown by H are corresponding second-order moment statistical characteristics, that is, it can be determined in step S103 by the LMMSE algorithm, and the target statistical characteristics of the signal to be detected include first-order moment statistical characteristics

And second moment statistical features (H)^HH+I)^- ¹H^HH。

In a possible implementation manner, in step S103, the process of determining the target statistical characteristic of the signal to be detected according to the received signal and the filter parameter of the received signal may specifically include: randomly sampling the received signal according to the filtering parameter of the received signal to obtain a sampling result, wherein the sampling result is used for indicating discrete information of the posterior probability of the signal to be detected; and calculating the target statistical characteristics of the signal to be detected according to the sampling result. The random sampling is used for selecting a plurality of constellation combination paths with the minimum search metric value according to the maximum probability, namely determining the target statistical characteristics of the statistical information for indicating the posterior probability of the signal to be detected.

Specifically, the process of randomly sampling the received signal according to the filtering parameter of the received signal to obtain a sampling result may specifically include: and randomly sampling the received signal according to the filtering parameters of the received signal and a target sampling structure to obtain a sampling result, wherein the target sampling structure comprises a serial sampling structure and/or a parallel sampling structure. That is, the signal detection apparatus may randomly sample the received signal by the serial sampling structure and/or the parallel sampling structure to obtain a sampling result.

The sampling result obtained in the random sampling process in step S103 can be implemented by various algorithms, and an exemplary sampling algorithm is provided in this embodiment to obtain the received signal in step B2

And the filter parameter R is an example, and specifically includes:

and D1, estimating the signal to be detected layer by layer from bottom to top by utilizing the upper triangular structure of the matrix R in a serial inter-stream interference elimination mode (the estimation result of the ith layer is recorded as the estimation result of the ith layer)

And randomly quantizing the estimation result to candidate constellation points according to the modulation mode of each layer/stream and recording the candidate constellation points as

For example, the first method:

where Q () denotes the nearest constellation quantization operation, ε_iRepresenting a predetermined random sample number; the second method comprises the following steps:

where Q () denotes the most recent constellation quantization operation, Δ_iRepresenting a legal constellation random offset, randomly offsetting the current constellation to other constellation points, and giving the constellation points through pre-configuration; or by other means, and is not limited herein.

Step D2, repeating step D1 (i.e. configuring different random sampling numbers or offsets) for multiple independent times, so as to obtain multiple sets of sampling results, or sampling path results, which are expressed as:

wherein N is the total number of sampling times,

represents the sampling path result, where N_tIs the total number of streams of the signal to be detected.

Step D3, calculating the corresponding metric value of each sampling path result to get eta ═ eta₁,…,η_N]。

One possible calculation method is:

wherein | |²And (5) performing a modulus square operation. In addition, the metric value may be obtained by other equivalent calculations using the filtering parameter, which is not described herein again.

Optionally, the received signal obtained in the step B2 is outputted in the steps D1 to D3

And the filter parameters were replaced with the received signals obtained in step a3 of mode a, respectively

And filter parameters

Then, the steps D1 to D4 may be executed to obtain the sampling result and the corresponding metric value, or implemented by the method C and other methods, and the implementation process is the same as the steps D1 to D3, which is not described herein again.

Alternatively, the random sampling algorithm described above uses a serial sampling structure in steps D1, D2 and D3, and may be modified by using a parallel sampling structure to obtain the received signal in step A3

And filter parameters

For example, the details are as follows:

step E1, calculating a random sampling estimation result:

wherein epsilon represents a preset random perturbation vector.

Step E2, performing nearest constellation point quantization on the sampling result:

where Q (·) represents the most recent constellation quantization operation.

Step E3, repeating steps E1-E2 (configuring different random disturbance vectors epsilon) for multiple times independently, obtaining multiple groups of sampling results, and recording as:

wherein N is the total number of sampling times,

Step E4, calculating the metric corresponding to each sampling path to obtain η ═ η₁,…,η_N]。

One possible calculation method is:

wherein | |²And (5) performing a modulus square operation. In addition, it is also possible toThe metric value is obtained by other equivalent calculations using filter parameters, which are not described herein again.

In addition, after obtaining the sampling result, the signal detection apparatus may further calculate the target statistical characteristic of the signal to be detected according to the sampling result, where the sampling result is obtained based on the example of the random sampling algorithm

And η, the calculation process includes:

step F1, calculating the path normalization probability to obtain:

p＝softmax(η)

wherein p is used to indicate a path normalized probability row vector, each element of which represents a normalized probability value corresponding to the sampling path, softmax () represents an activation function for normalizing an input vector to a probability value, and the computational expression of the nth output value is

Eta is used to indicate the metric value corresponding to the sampling path.

Step F2, respectively calculating a first moment characteristic by dividing the real part and the imaginary part to obtain:

wherein, mu_r/iColumn vectors indicating first order moment components corresponding to real/imaginary parts of each stream, real/imag () indicating real/imaginary part-taking operations, p^TThe transpose used to indicate p.

Step F3, respectively calculating the second moment characteristics by dividing the real part and the imaginary part to obtain:

wherein the content of the first and second substances,

for indicating the corresponding second-order moment characteristics of real/imaginary parts per stream, e for indicating the full 1-row vector, p, with dimensions equal to the number of sampling paths, N^TThe transpose used to indicate p.

From the above calculation process, it can be determined in step S103 that the target statistical characteristic of the signal to be detected includes the first moment characteristic μ_r/iAnd second moment characteristics

And S104, outputting target bit soft information corresponding to the signal to be detected after the target statistical characteristics are processed at least once by adopting a neural network.

In this embodiment, after obtaining the target statistical characteristic in step S103, the signal detection apparatus uses the target statistical characteristic as an input of the neural network, and outputs the target statistical characteristic to obtain target bit soft information corresponding to the signal to be detected after performing at least one processing on the target statistical characteristic by the neural network, thereby completing the detection process of the signal to be detected in the received signal.

The training process of the neural network referred to in the present application will be exemplarily described below.

Referring to fig. 4-1, a system architecture 100 is provided in an embodiment of the invention. The data collection device 160 is configured to collect the received signal and/or the target statistical characteristic of the signal to be detected in the received signal as input data and store the input data in the database 130, and the training device 120 generates the target model/rule 101 based on the input data maintained in the database 130. How the training device 120 derives the target model/rule 101 based on the input data will be described in more detail below.

The operation of each layer in the deep neural network can be expressed mathematically

To describe: working from each layer in a physical-level deep neural network can be understood as performing a transformation of an input space into an output space by five operations on the input space (a set of input vectors)(i.e., row space to column space of the matrix), these five operations include: 1. ascending/descending dimensions; 2. zooming in/out; 3. rotating; 4. translating; 5. "bending". Wherein 1,2, 3 are operated by

The operation of 4 is completed by + b, and the operation of 5 is realized by a (). The expression "space" is used herein because the object being classified is not a single thing, but a class of things, and space refers to the collection of all individuals of such things. Where W is a weight vector, each value in the vector representing a weight value for a neuron in the layer of neural network. The vector W determines the spatial transformation of the input space into the output space described above, i.e. the weight W of each layer controls how the space is transformed. The purpose of training the deep neural network is to finally obtain the weight matrix (the weight matrix formed by the vectors W of many layers) of all the layers of the trained neural network. Therefore, the training process of the neural network is essentially a way of learning the control space transformation, and more specifically, the weight matrix.

Because it is desirable that the output of the deep neural network is as close as possible to the value actually desired to be predicted, the weight vector of each layer of the neural network can be updated by comparing the predicted value of the current network with the value actually desired to be predicted, and then according to the difference between the predicted value and the value actually desired to be predicted (of course, there is usually an initialization process before the first update, that is, parameters are pre-configured for each layer in the deep neural network). For example, if the predicted value of the network is high, the weight vector is adjusted to make the predicted value lower, and the adjustment is continued until the neural network can predict the real desired target value. Therefore, it is necessary to define in advance "how to compare the difference between the predicted value and the target value", which are loss functions (loss functions) or objective functions (objective functions), which are important equations for measuring the difference between the predicted value and the target value. Taking the loss function as an example, if the higher the output value (loss) of the loss function indicates the larger the difference, the training of the deep neural network becomes the process of reducing the loss as much as possible.

The goal models/rules derived by the training device 120 may be applied in different systems or devices. In FIG. 4-1, the execution device 110 is configured with an I/O interface 112 to interact with data from an external device, and a "user" may input data to the I/O interface 112 via a client device 140.

The execution device 110 may call data, code, etc. from the data storage system 150 and may store data, instructions, etc. in the data storage system 150. The signal detection apparatus in this embodiment of the application may include a processing procedure of the execution device 110 to implement a neural network, or a processing procedure of the neural network implemented by externally connecting the execution device 110, which is not limited herein.

The calculating module 111 processes the input data using the target model/rule 101, for example, in step S104, the calculating module 111 processes the input target statistical characteristics at least once to obtain the target bit soft information corresponding to the signal to be detected

Finally, the I/O interface 112 returns the results of the processing to the client device 140 for presentation to the user.

Further, the training device 120 may generate corresponding target models/rules 101 based on different data for different targets to provide better results to the user.

It should be noted that fig. 4-1 is only a schematic diagram of a system architecture provided by an embodiment of the present invention, and the position relationship between the devices, modules, and the like shown in the diagram does not constitute any limitation, for example, in fig. 4-1, the data storage system 150 is an external memory with respect to the execution device 110, and in other cases, the data storage system 150 may be disposed in the execution device 110.

In a possible implementation manner, in step S104, the signal detection apparatus may perform at least one processing procedure on the target statistical characteristic of the signal to be detected in multiple ways in the neural network, and output corresponding target bit soft information, specifically, the target bit soft information of the signal to be detected may be obtained through mapping processing in the neural network after the target statistical characteristic of the signal to be detected is obtained.

Illustratively, the mapping process is illustrated in FIG. 4-2, with the target statistical feature (first moment feature μ) in the input layer_r/iAnd second moment characteristics

) As input data, after being subjected to mapping processing of the hidden layer, the input data is output as bit soft information. The hidden layer may have a plurality of computing nodes, and for different modulation modes, the number of the computing nodes may have different values (e.g., 4, 8, and 16 …). The number of output nodes is used for indicating the number of modulation bits and is related to the modulation mode. Illustratively, 256QAM modulation is taken as an example, and the bit mapping adopts gray mapping, i.e. each constellation symbol corresponds to 8 bits (since 2 is used)⁸256), under gray mapping, the real part and the imaginary part of each constellation symbol are respectively and independently mapped, i.e. each constellation symbol occupies 4 bits, so that the output node is 4; take 16QAM modulation as an example, and the bit mapping adopts gray mapping, i.e. each constellation symbol corresponds to 4 bits (since 2 bits⁴16), the real part and the imaginary part of each constellation symbol are mapped independently, i.e. each takes 2 bits, under gray mapping, so the output node is 2.

In a possible implementation manner, in step S104, the signal detection apparatus may perform at least one processing process on a target statistical characteristic of a signal to be detected in multiple ways in the neural network, and output corresponding target bit soft information, where after obtaining the target statistical characteristic of the signal to be detected, after obtaining a first statistical characteristic of statistical information used for indicating the target bit soft information through fusion processing in the neural network, mapping the first statistical characteristic, and outputting the target bit soft information of the signal to be detected.

Taking the example shown in fig. 4-2 as an example, the process of obtaining the first statistical characteristic of the statistical information for indicating the target bit soft information through the fusion processing in the neural network specifically includes: and combining multiple types (first-order moment characteristics and second-order moment characteristics) of the input target statistical characteristics into one type, so that the combined characteristic dimension is larger than that of the input target statistical characteristics before combination. Illustratively, a first moment may be inputTaking absolute value operation to obtain | mu | and performing linear transformation, and multiplying the transformation result by second-order moment nonlinear transformation value (here, the second-order moment nonlinear transformation value is used as the second-order moment nonlinear transformation value)

For example, other follow-up sigma may be optionally designed²A function form which becomes larger and monotonously smaller, or a form after network structure transformation), and then a calculation result is obtained through a calculation process of a nonlinear activation function and is used as the output of the hidden layer. The activation function may be a ReLu activation function and/or a softplus activation function and/or a softmax activation function, and is implemented in the form of f (x) ═ ln (1+ e), for example, as a softplus activation function^x)。

In a possible implementation manner, in step S104, the signal detection apparatus may perform at least one processing process on the target statistical characteristic of the signal to be detected in multiple ways in the neural network, and output corresponding target bit soft information, where after the target statistical characteristic of the signal to be detected is obtained, after the first bit soft information corresponding to the signal to be detected is obtained through mapping processing in the neural network, the first bit soft information corresponding to the signal to be detected is corrected according to the first value range, and the target bit soft information of the signal to be detected is output.

Taking fig. 4-2 as an example, the process of performing modification processing on the first bit soft information corresponding to the signal to be detected according to the first value range may specifically include: determining different first value ranges according to different input target statistical characteristics, for example, the first value ranges may be (a, 0), or (0, b), or (c, d), where a is less than 0, b is greater than 0, and d is greater than c; further, corresponding output symbol adjustment (i.e. possible phase inversion) is performed according to the first value range, or the value range of the modified contrast extra-soft information is limited by the maximum and minimum values. Illustratively, the first moment μmay be input based on_r/iPositive or negative sign of (1), on the output low bit result (i.e. eta in fig. 4-2)₀) Making a correction, i.e. if μ is greater than 0, η₀And (5) inverting and outputting the result.

In a possible implementation manner, in step S104, the signal detection apparatus may perform at least one processing process on a target statistical characteristic of a signal to be detected in multiple ways in the neural network, and output corresponding target bit soft information, wherein after the target statistical characteristic of the signal to be detected is obtained, a second statistical characteristic of statistical information used for indicating second bit soft information corresponding to the signal to be detected is obtained through fusion processing in the neural network, and after the second statistical characteristic is subjected to mapping processing to obtain second bit soft information, a value of the second bit soft information is corrected according to a second value range, and the target bit soft information of the signal to be detected is output.

In this embodiment, the processes of the mapping process, the fusion process, and the modification process may refer to the foregoing description about the content shown in fig. 4-2, and are not described herein again.

In a possible implementation manner, in the processing process of the neural network, the used weight coefficients are obtained by training the preset signals after the data labels are generated through the preset algorithm, and compared with the processing process in which actual transmitted real data are used as the labels in the neural network and the calculated weight coefficients participate, the design of the loss function can be simplified, and meanwhile, the processing result of the neural network is optimized to approach the optimal performance of the preset algorithm. Specifically, the preset algorithm may include a maximum likelihood detection MLD algorithm, or a linear minimum mean square error LMMSE algorithm, or other algorithm implementation, which is not limited herein.

Preferably, the weight coefficient of the neural network may be obtained by training a preset signal after the neural network generates a data tag through a Maximum Likelihood Detection (MLD) algorithm. Specifically, in the signal detection processing process, under the assumption of gaussian noise and the assumption of ideal channel information, MLD is the optimal detection algorithm, so that the weight coefficient obtained by training the preset signal after generating the data tag by the MLD algorithm is applied to the neural network, and the subsequent processing process of the neural network can be optimized.

In this embodiment, after receiving a received signal, a signal detection apparatus determines channel state information of the received signal, where the received signal includes a signal to be detected; determining a filtering parameter of the received signal according to the channel state information; determining a target statistical characteristic of the signal to be detected according to the received signal and the filtering parameter of the received signal, wherein the target statistical characteristic is used for indicating statistical information of the posterior probability of the signal to be detected; and outputting target bit soft information corresponding to the signal to be detected after the target statistical characteristics are processed at least once by adopting a neural network. The signal detection device determines the statistical characteristics of the signal to be detected according to the filtering parameters of the received signal, the neural network is adopted to process the target statistical characteristics at least once and then output target bit soft information corresponding to the signal to be detected, namely the neural network is utilized to process the statistical characteristics of the signal to be detected and then output corresponding bit soft information.

Fig. 4-3 is a schematic diagram of a signal detection method in an embodiment of the present application, and as shown in fig. 4-3, the signal detection method includes the following steps.

S1, carrying out linear processing on the received signal according to the channel information and the noise covariance information;

in this embodiment, the signal detection apparatus obtains the received signal, may obtain channel information H (i.e., channel state information) through the processing procedure of formula (2), and performs linear processing on the received signal y according to the noise covariance information I to obtain a linear processing result.

In the linear processing procedure of step S1, the method a, the method B, the method C and their related steps in step S101 can be referred to for implementation, and are not described herein again.

S2, carrying out random sampling according to the result after linear processing and the equivalent covariance matrix;

in this embodiment, the signal detection apparatus performs random sampling according to the result after the linear processing and the equivalent covariance matrix to obtain a random sampling result.

In the random sampling process of step S2, reference may be made to the sampling process of step S103, that is, the implementation processes of step D1 to step D3, step E1 to step E4 and related steps, which are not described herein again.

S3, calculating a moment characteristic value corresponding to each sampling dimension according to the sampling result;

in this embodiment, the signal detection device calculates a moment eigenvalue corresponding to each sampling dimension according to the sampling result.

The process of calculating the moment eigenvalue in step S3 may refer to the implementation processes of step F1 to step F3 and their related steps in step S103, and will not be described herein again.

S4, mapping the moment features into final bit LLR values and outputting the bit LLR values;

in this embodiment, each moment feature pair obtained in step S3 is sequentially mapped in the neural network into a corresponding bit LLR value to be output according to the modulation mode of the received signal, and the mapping method is to pass the input through a given neural network.

The mapping process in the neural network (for example, shown in fig. 4-2) in step S4 may refer to the implementation process in step S104, and is not described herein again.

In the embodiment, in the processing part, only single Cholesky decomposition is equivalently needed, the complexity is obviously lower than that of a scheme based on multiple QR decompositions, in addition, in the LLR mapping part, the moment characteristics are directly mapped into LLR values by utilizing a neural network, and the comparison operation in the traditional max-log algorithm is replaced by more universal and efficient multiply-add operation; furthermore, the performance of the computing process approaches the optimal MLD performance, and a linear processing method with lower complexity in an actual scene has obvious performance gain.

Fig. 5 is a schematic diagram of a signal detection method in an embodiment of the present application, and as shown in fig. 4-3, the signal detection method includes the following steps.

S201, determining channel state information of a received signal;

The implementation process in step S201 may refer to the related steps in step S101, and is not described herein again.

S202, determining a filtering parameter of the received signal according to the channel state information;

in this embodiment, the signal detection apparatus may further determine the filtering parameter of the received signal according to the channel state information determined in step S201.

The implementation process in step S202 may refer to the related steps in step S102, and is not described herein again.

S203, randomly sampling the received signal according to the filtering parameter of the received signal to obtain a sampling result;

in this embodiment, the signal detection apparatus performs random sampling on the received signal according to the filtering parameter of the received signal obtained in step S202 to obtain a sampling result. Wherein the sampling result is used for indicating discrete information of the posterior probability of the signal to be detected.

In a possible implementation manner, the randomly sampling the received signal according to the filtering parameter of the received signal to obtain a sampling result may specifically include: and randomly sampling the received signal according to the filtering parameters of the received signal and a target sampling structure to obtain a sampling result, wherein the target sampling structure comprises a serial sampling structure and/or a parallel sampling structure. That is, the signal detection apparatus may randomly sample the received signal by the serial sampling structure and/or the parallel sampling structure to obtain a sampling result.

The sampling result obtained in the random sampling process in step S203 may be implemented by various algorithms, and reference may be made to the implementation process of obtaining the sampling result (or the sampling path result) by calculation in the sampling process in step S103, that is, in steps D1 to D3, steps E1 to E4, and related steps thereof, which are not described herein again.

And S204, obtaining target bit soft information corresponding to the signal to be detected according to the sampling result.

In this embodiment, the signal detection apparatus further calculates target soft bit information corresponding to the signal to be detected according to the sampling result obtained in step S203.

In a possible implementation manner, the process of obtaining the target soft bit information corresponding to the signal to be detected according to the sampling result may be implemented by the foregoing formula (4). For example, taking the sampling result calculated through steps E1 to E4 as an example, the sampling result may be expressed as:

wherein N is the total number of sampling times,

Further, considering the additive noise effect of gaussian, to solve equation (1), the soft bit information can be approximately calculated by using max-log algorithm, i.e. bit-by-bit from the set

The minimum metric value is selected as output and recorded as

Namely, expressed by formula (4):

then, subtracting the result of taking 0 and 1 for each bit to obtain the final bit log-likelihood ratio (LLR), namely obtaining the target bit soft information corresponding to the signal to be detected as

And/or

In a possible implementation manner, in step S204, the implementation process of calculating the target bit soft information corresponding to the signal to be detected may also refer to the implementation through the neural network in step S104, which is not described herein again.

In this embodiment, after receiving a received signal, a signal detection apparatus determines channel state information of the received signal, where the received signal includes a signal to be detected; determining a filtering parameter of the received signal according to the channel state information; randomly sampling the received signal according to the filtering parameter of the received signal to obtain a sampling result, wherein the sampling result is used for indicating discrete information of the posterior probability of the signal to be detected; and obtaining target bit soft information corresponding to the signal to be detected according to the sampling result. The signal detection device carries out random sampling on the received signal according to the filtering parameters of the received signal to obtain the sampling result of the discrete information used for indicating the posterior probability of the signal to be detected.

The embodiments of the present application have been described above from the perspective of methods, and the signal detection apparatus in the embodiments of the present application will be described below from the perspective of specific apparatus implementation.

Referring to fig. 6, an embodiment of the present application provides a schematic diagram of a signal detection apparatus 600, wherein the signal detection apparatus 600 at least includes a determination unit 601 and a processing unit 602.

In one implementation of the signal detection device 600,

the determining unit 601 is configured to determine channel state information of a received signal, where the received signal includes a signal to be detected;

the determining unit 601 is further configured to determine a filtering parameter of the received signal according to the channel state information;

the determining unit 601 is further configured to determine a target statistical characteristic of the signal to be detected according to the received signal and the filter parameter of the received signal, where the target statistical characteristic is used to indicate statistical information of a posterior probability of the signal to be detected;

the processing unit 602 is further configured to output target bit soft information corresponding to the signal to be detected after the target statistical characteristic is processed at least once by using a neural network.

In a possible implementation manner, the processing unit 602 is specifically configured to:

In one possible implementation, the target activation function of the neural network comprises a ReLu activation function and/or a softplus activation function and/or a softmax activation function.

In a possible implementation manner, the weight coefficient of the neural network is obtained by training a preset signal after the neural network generates a data tag through a preset algorithm.

In one possible implementation, the predetermined algorithm includes a Maximum Likelihood Detection (MLD) algorithm, or alternatively, a Linear Minimum Mean Square Error (LMMSE) algorithm.

In a possible implementation manner, the determining unit 601 is specifically configured to:

and determining the filtering parameters of the received signal in a target mode according to the channel state information, wherein the target mode comprises Cholesky decomposition processing, QR decomposition processing or linear detection processing.

and/or the presence of a gas in the gas,

It should be noted that, for details of the information execution process of the units of the signal detection apparatus 600, reference may be specifically made to the description of the foregoing method embodiments in the present application, and details are not described here again.

In one implementation of the signal detection device 600,

the determining unit 601 is further configured to perform random sampling on the received signal according to the filtering parameter of the received signal to obtain a sampling result, where the sampling result is used to indicate discrete information of a posterior probability of the signal to be detected;

the processing unit 602 is further configured to obtain target bit soft information corresponding to the signal to be detected according to the sampling result.

and/or the presence of a gas in the gas,

Referring to fig. 7, a schematic structural diagram of a communication device according to the foregoing embodiments is provided in an embodiment of the present application, where the communication device may specifically be a network device in the foregoing embodiments, and the structure of the communication device may refer to the structure shown in fig. 7.

The communication device includes at least one processor 711, at least one memory 712, at least one transceiver 713, at least one network interface 714, and one or more antennas 715. The processor 711, the memory 712, the transceiver 713 and the network interface 714 are connected, for example, by a bus, and in this embodiment, the connection may include various interfaces, transmission lines or buses, which is not limited in this embodiment. The antenna 715 is connected to the transceiver 713. The network interface 714 is used to enable the communication apparatus to connect with other communication devices via communication links, for example, the network interface 714 may include a network interface between the communication apparatus and a core network device, such as an S1 interface, and the network interface may include a network interface between the communication apparatus and other network devices (such as other access network devices or core network devices), such as an X2 or Xn interface.

The processor 711 is mainly used for processing communication protocols and communication data, controlling the entire communication device, executing software programs, and processing data of the software programs, for example, for supporting the communication device to perform the actions described in the embodiments. The communication device may include a baseband processor for processing communication protocols and communication data, and a central processor for controlling the entire network device, executing software programs, and processing data of the software programs. The processor 711 in fig. 7 may integrate functions of a baseband processor and a central processing unit, and those skilled in the art will understand that the baseband processor and the central processing unit may also be independent processors, and are interconnected through a bus or the like. Those skilled in the art will appreciate that a network device may include multiple baseband processors to accommodate different network formats, multiple central processors to enhance its processing capabilities, and various components of the network device may be connected by various buses. The baseband processor can also be expressed as a baseband processing circuit or a baseband processing chip. The central processing unit can also be expressed as a central processing circuit or a central processing chip. The function of processing the communication protocol and the communication data may be built in the processor, or may be stored in the memory in the form of a software program, and the processor executes the software program to realize the baseband processing function.

The memory is used primarily for storing software programs and data. The memory 712 may be separate and coupled to the processor 711. Alternatively, the memory 712 may be integrated with the processor 711, for example, within a chip. The memory 712 can store program codes for executing the technical solutions of the embodiments of the present application, and is controlled by the processor 711, and the executed computer program codes can also be regarded as drivers of the processor 711.

Fig. 7 shows only one memory and one processor. In an actual network device, there may be multiple processors and multiple memories. The memory may also be referred to as a storage medium or a storage device, etc. The memory may be a memory element on the same chip as the processor, that is, an on-chip memory element, or a separate memory element, which is not limited in this embodiment.

A transceiver 713 may be used to support reception or transmission of radio frequency signals between the communication device and the terminal, and the transceiver 713 may be coupled to an antenna 715. The transceiver 713 includes a transmitter Tx and a receiver Rx. Specifically, one or more antennas 715 may receive a radio frequency signal, and the receiver Rx of the transceiver 713 is configured to receive the radio frequency signal from the antennas, convert the radio frequency signal into a digital baseband signal or a digital intermediate frequency signal, and provide the digital baseband signal or the digital intermediate frequency signal to the processor 711, so that the processor 711 performs further processing on the digital baseband signal or the digital intermediate frequency signal, such as demodulation processing and decoding processing. In addition, the transmitter Tx in the transceiver 713 is also used to receive a modulated digital baseband signal or a digital intermediate frequency signal from the processor 711, convert the modulated digital baseband signal or the digital intermediate frequency signal into a radio frequency signal, and transmit the radio frequency signal through one or more antennas 715. Specifically, the receiver Rx may selectively perform one or more stages of down-mixing and analog-to-digital conversion processes on the rf signal to obtain a digital baseband signal or a digital intermediate frequency signal, wherein the order of the down-mixing and analog-to-digital conversion processes is adjustable. The transmitter Tx may selectively perform one or more stages of up-mixing and digital-to-analog conversion processes on the modulated digital baseband signal or the modulated digital intermediate frequency signal to obtain the rf signal, where the order of the up-mixing and the digital-to-analog conversion processes is adjustable. The digital baseband signal and the digital intermediate frequency signal may be collectively referred to as a digital signal.

A transceiver may also be referred to as a transceiver unit, transceiver, transceiving means, etc. Optionally, a device for implementing a receiving function in the transceiver unit may be regarded as a receiving unit, and a device for implementing a sending function in the transceiver unit may be regarded as a sending unit, that is, the transceiver unit includes a receiving unit and a sending unit, the receiving unit may also be referred to as a receiver, an input port, a receiving circuit, and the like, and the sending unit may be referred to as a transmitter, a sending circuit, and the like.

It should be noted that the communication apparatus shown in fig. 7 may be specifically configured to implement the steps implemented by the network device in the method embodiments corresponding to fig. 3 to fig. 5, and details are not repeated here.

The present application further provides a computer-readable storage medium storing one or more computer-executable instructions, which when executed by a processor, perform the method as described in the possible implementation manner of the communication device in the foregoing embodiments, where the communication device may specifically be the communication device in the foregoing embodiments.

The embodiments of the present application also provide a computer program product (or computer program) storing one or more computers, and when the computer program product is executed by the processor, the processor executes the method that may be implemented by the communication apparatus, where the communication apparatus may specifically be the communication apparatus in the foregoing embodiments.

An embodiment of the present application further provides a chip system, where the chip system includes a processor, and is configured to support a communication device to implement functions related to possible implementation manners of the communication device. In one possible design, the system-on-chip may further include a memory, which stores program instructions and data necessary for the communication device. The chip system may be formed by a chip, or may include a chip and other discrete devices, where the communication device may specifically be the signal detection device in the foregoing embodiment.

An embodiment of the present application further provides a network system architecture, where the network system architecture includes the communication device described above, and the communication device may specifically be the signal detection device in any one of the foregoing embodiments.

In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.

The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.

Claims

1. A method of signal detection, the method comprising:

determining channel state information of a received signal, wherein the received signal comprises a signal to be detected;

determining a filtering parameter of the received signal according to the channel state information;

determining a target statistical characteristic of the signal to be detected according to the received signal and the filtering parameter of the received signal, wherein the target statistical characteristic is used for indicating statistical information of the posterior probability of the signal to be detected;

and outputting target bit soft information corresponding to the signal to be detected after the target statistical characteristics are processed at least once by adopting a neural network.

2. The method according to claim 1, wherein the outputting the target bit soft information corresponding to the signal to be detected after the target statistical characteristic is processed at least once by using the neural network comprises:

3. The method according to claim 1, wherein the outputting the target bit soft information corresponding to the signal to be detected after the target statistical characteristic is processed at least once by using the neural network comprises:

4. The method according to claim 1, wherein the outputting the target bit soft information corresponding to the signal to be detected after the target statistical characteristic is processed at least once by using the neural network comprises:

5. The method according to claim 1, wherein the outputting the target bit soft information corresponding to the signal to be detected after the target statistical characteristic is processed at least once by using the neural network comprises:

and correcting the value of the second bit soft information corresponding to the signal to be detected according to a second value range, and outputting the target bit soft information corresponding to the signal to be detected.

6. The method according to any one of claims 1 to 5,

the target activation function of the neural network comprises a ReLu activation function and/or a softplus activation function and/or a softmax activation function.

7. The method according to any one of claims 1 to 6,

the weight coefficient of the neural network is obtained by training a preset signal after the neural network generates a data tag through a preset algorithm.

8. The method of claim 7, wherein the predetermined algorithm comprises a Maximum Likelihood Detection (MLD) algorithm or a Linear Minimum Mean Square Error (LMMSE) algorithm.

9. The method according to any one of claims 1 to 8, wherein the determining the filtering parameters of the received signal according to the channel state information comprises:

and determining the filtering parameters of the received signals in a target mode according to the channel state information, wherein the target mode comprises Cholesky decomposition processing, QR decomposition processing, SQR decomposition processing or linear detection processing.

10. The method of claims 1 to 9, wherein the determining the filtering parameters of the received signal according to the channel state information comprises:

the received signals are normalized according to second preset parameters to obtain first received signals;

and/or the presence of a gas in the gas,

the determining the target statistical characteristics of the signal to be detected according to the received signal and the filter parameter of the received signal includes:

the received signals are normalized according to a third preset parameter to obtain second received signals;

the filtering parameters of the received signals are normalized according to a fourth preset parameter to obtain first filtering parameters;

11. The method according to any one of claims 1 to 10, wherein the determining the target statistical characteristic of the signal to be detected according to the received signal and the filter parameter of the received signal comprises:

12. The method of claim 11, wherein the randomly sampling the received signal according to the filtering parameter of the received signal to obtain a sampling result comprises:

13. A method of signal detection, the method comprising:

and obtaining target bit soft information corresponding to the signal to be detected according to the sampling result.

14. The method of claim 13, wherein the determining the filtering parameters of the received signal according to the channel state information comprises:

15. The method according to claim 13 or 14, wherein the determining the filtering parameters of the received signal according to the channel state information comprises:

and/or the presence of a gas in the gas,

16. The method according to any one of claims 13 to 15, wherein the randomly sampling the received signal according to the filtering parameter of the received signal to obtain a sampling result comprises:

17. A signal detection apparatus, characterized in that the apparatus comprises a determination unit and a processing unit:

the determining unit is used for determining channel state information of a received signal, wherein the received signal comprises a signal to be detected;

the determining unit is further configured to determine a target statistical characteristic of the signal to be detected according to the received signal and the filter parameter of the received signal, where the target statistical characteristic is used to indicate statistical information of a posterior probability of the signal to be detected;

and the processing unit is used for outputting the target bit soft information corresponding to the signal to be detected after the target statistical characteristics are processed at least once by adopting a neural network.

18. The apparatus according to claim 17, wherein the processing unit is specifically configured to:

19. The apparatus according to claim 17, wherein the processing unit is specifically configured to:

20. The apparatus according to claim 17, wherein the processing unit is specifically configured to:

21. The apparatus according to claim 17, wherein the processing unit is specifically configured to:

22. The apparatus of any one of claims 17 to 21,

23. The apparatus of any one of claims 17 to 22,

24. The apparatus of claim 23, wherein the predetermined algorithm comprises a Maximum Likelihood Detection (MLD) algorithm or a Linear Minimum Mean Square Error (LMMSE) algorithm.

25. The apparatus according to any one of claims 17 to 24, wherein the determining unit is specifically configured to:

26. The apparatus according to any one of claims 17 to 25, wherein the determining unit is specifically configured to:

and/or the presence of a gas in the gas,

27. The apparatus according to any one of claims 17 to 26, wherein the determining unit is specifically configured to:

28. The apparatus according to claim 27, wherein the determining unit is specifically configured to:

29. A signal detection apparatus, characterized in that the apparatus comprises a determination unit and a processing unit:

30. The apparatus according to claim 29, wherein the determining unit is specifically configured to:

31. The apparatus according to claim 29 or 30, wherein the determining unit is specifically configured to:

and/or the presence of a gas in the gas,

32. The apparatus according to claim 29 or 31, wherein the determining unit is specifically configured to:

33. A communication apparatus comprising at least one processor and interface circuitry, wherein,

the interface circuitry to provide programming or instructions to the at least one processor;

the at least one processor is configured to execute the program or instructions to cause the communication apparatus to implement the method of any one of claims 1 to 12 or to cause the communication apparatus to implement the method of any one of claims 13 to 16.

34. A computer readable storage medium having stored thereon instructions which, when executed by a computer, carry out the method of any one of claims 1 to 12 or carry out the method of any one of claims 13 to 16.

35. A communication system, characterized in that the communication system comprises a signal processing device according to any of claims 17 to 28, or in that the communication system comprises a signal processing device according to any of claims 29 to 32, or in that the communication system comprises a communication device according to claim 33.