Signal processing methods, apparatus, equipment, storage media and program products

Abstract

This application relates to a signal processing method, apparatus, device, readable storage medium, and program product. The method includes: acquiring a baseband common-mode algorithm model for wireless communication and the local function corresponding to each baseband module in the receiver; converting the baseband common-mode algorithm model into the propagation function of each baseband module based on the local function corresponding to each baseband module; processing the signal to be processed received at the receiver using the propagation function corresponding to each baseband module to obtain bit soft information of the signal to be processed; and determining the original transmitted symbol corresponding to the signal to be processed based on the bit soft information. This method can improve the system's bit error rate performance.

Description

Technical Field

[0001] This application relates to the field of communication technology, and in particular to a signal processing method, apparatus, device, storage medium, and program product. Background Technology

[0002] Currently, 5G and 6G mobile communications possess technical characteristics such as high bandwidth, massive connectivity, high reliability, and low latency, enabling deep integration and comprehensive penetration into vertical industries. Because vertical industries have diverse application scenarios and varied needs, 5G and 6G mobile communications require customization of base stations and terminals based on these application scenarios.

[0003] In related technologies, due to the customization of base stations and terminals, the baseband signal processing schemes of different base stations and terminals vary greatly in the algorithms of each baseband module, requiring individual optimization of the algorithms of each baseband module. Because the algorithms of each baseband module are based on different message dimensions (e.g., logarithmic domain messages and real number domain messages), the interfaces between baseband modules are inconsistent, resulting in the loss of a large amount of useful information at these interfaces, thus limiting the overall bit error rate performance of the system. Summary of the Invention

[0004] Therefore, it is necessary to provide a signal processing method, apparatus, device, readable storage medium, and program product that can improve the bit error rate performance of the system in response to the above-mentioned technical problems.

[0005] In a first aspect, this application provides a signal processing method, comprising:

[0006] Obtain the baseband common-mode algorithm model for wireless communication and the local function corresponding to each baseband module in the receiver;

[0007] Based on the local function corresponding to each baseband module, the baseband common mode algorithm model is converted into the propagation function of each baseband module respectively;

[0008] The signal to be processed received by the receiving end is processed using the propagation function corresponding to each baseband module to obtain the bit soft information of the signal to be processed.

[0009] Based on the bit soft information, determine the original transmission symbol corresponding to the signal to be processed.

[0010] In one embodiment, before obtaining the baseband common-mode algorithm model for wireless communication and the local function corresponding to each baseband module in the receiver, the method further includes:

[0011] Obtain the baseband signal receiving model;

[0012] According to the Bayesian probability formula, the baseband signal receiving model is decomposed to obtain a system-level baseband algorithm model. The system-level baseband algorithm model is used to solve for the estimated value of the original transmitted symbol based on the signal to be processed.

[0013] The signal to be processed is split into local signals to be processed corresponding to each baseband module, and the system-level baseband algorithm model is converted into a module-level baseband algorithm model based on the local signals to be processed corresponding to each baseband module.

[0014] The local signal to be processed is split into the input signals of the corresponding functional nodes in the factor graph, and the module-level baseband algorithm model is converted into a node-level baseband algorithm model according to the input signals of the corresponding functional nodes in the factor graph.

[0015] Based on the probability information transmitted along the edges between the functional nodes and the variable nodes in the factor graph, the node-level baseband algorithm model is converted into the baseband common-mode algorithm model.

[0016] The variable nodes are used to characterize the local signal to be processed by the corresponding baseband module, and the functional nodes are used to characterize the constraints of the corresponding baseband module on the local signal to be processed. The system-level baseband algorithm model includes at least one of the following: maximum a posteriori probability (MAP) algorithm model, message passing algorithm model, and linear transformation algorithm model.

[0017] In one embodiment, the baseband common-mode algorithm model includes a function to be evolved; the step of converting the baseband common-mode algorithm model into the propagation function of each baseband module according to the local function corresponding to each baseband module includes:

[0018] The function to be evolved in the baseband common mode algorithm model is replaced with the local function corresponding to each baseband module, so as to convert the baseband common mode algorithm model into the propagation function of each baseband module.

[0019] In one embodiment, the step of processing the signal to be processed received by the receiving end using the propagation functions of each baseband module to obtain the target bit soft information of the signal to be processed includes:

[0020] The soft information generated by the propagation function of each baseband module is processed through multiple loop iterations between the propagation functions of each baseband module until the early stopping condition is met or the number of loops reaches the maximum number of iterations, so as to obtain the bit soft information of the signal to be processed.

[0021] The signal to be processed is the input of the first propagation function in the first iteration. Each iteration includes forward message passing and backward message passing. The soft information generated by the propagation function of the baseband module corresponding to each passing node in the forward message passing and backward message passing is used as the input of the propagation function of the baseband module corresponding to the next passing node.

[0022] In one embodiment, the propagation function includes at least one of the following: Gaussian approximation message propagation (GAMP) algorithm function, confidence propagation (BP) detection algorithm function, soft modulation and demodulation algorithm, and BP decoding algorithm function.

[0023] In one embodiment, the forward messaging includes:

[0024] The GAMP algorithm function is used to process the pilot received signal or the signal feedback information from the previous loop to generate the channel information for the current loop.

[0025] The BP detection algorithm function is used to process the channel information of the current loop and the signal to be processed to generate symbol soft information for the current loop.

[0026] A soft modulation and demodulation algorithm is used to demodulate the symbol soft information of the current loop to generate the demodulated information of the current loop.

[0027] The demodulated information of the current loop is interleaved to generate the bit soft information of the current loop.

[0028] In one embodiment, the backward message passing includes:

[0029] The BP decoding algorithm function is used to process the bit information of the current loop and generate the bit prior information of the current loop.

[0030] The bit prior information of the current loop is deinterleaved to generate deinterleaved bit prior information for the current loop;

[0031] A soft modulation and demodulation algorithm is used to modulate the deinterleaved bit prior information of the current cycle to generate symbol prior soft information of the current cycle.

[0032] The BP detection algorithm function is used to process the channel information of the current loop and the signal to be processed to generate the signal feedback information of the current loop.

[0033] Secondly, this application provides a signal processing apparatus, comprising:

[0034] The acquisition module acquires the baseband common-mode algorithm model for wireless communication and the local function corresponding to each baseband module in the receiver.

[0035] The conversion module is used to convert the baseband common mode algorithm model into the propagation function of each baseband module according to the local function corresponding to each baseband module.

[0036] The processing module is used to process the signal to be processed received by the receiving end using the propagation function corresponding to each baseband module to obtain the bit soft information of the signal to be processed; and to determine the original transmission symbol corresponding to the signal to be processed based on the bit soft information.

[0037] In one embodiment, the signal processing apparatus further includes:

[0038] A model generation module is used to acquire a baseband signal receiving model; decompose the baseband signal receiving model according to the Bayesian probability formula to obtain a system-level baseband algorithm model, which is used to solve for the estimated value of the original transmitted symbol based on the signal to be processed; decompose the signal to be processed into local signals corresponding to each baseband module, and convert the system-level baseband algorithm model into a module-level baseband algorithm model based on the local signals corresponding to each baseband module; decompose the local signals to be processed into the input signals of the corresponding functional nodes in the factor graph, and convert the module-level baseband algorithm model into a node-level baseband algorithm model based on the input signals of the corresponding functional nodes in the factor graph; and convert the node-level baseband algorithm model into the baseband common-mode algorithm model based on the probability information transmitted along the edges between the functional nodes and the variable nodes in the factor graph.

[0039] The variable nodes are used to characterize the local signal to be processed by the corresponding baseband module, and the functional nodes are used to characterize the constraints of the corresponding baseband module on the local signal to be processed. The system-level baseband algorithm model includes at least one of the following: maximum a posteriori probability (MAP) algorithm model, message passing algorithm model, and linear transformation algorithm model.

[0040] In one embodiment, the baseband common-mode algorithm model includes a function to be evolved; the conversion module is further configured to replace the function to be evolved in the baseband common-mode algorithm model with the local function corresponding to each baseband module, so as to convert the baseband common-mode algorithm model into the propagation function of each baseband module.

[0041] In one embodiment, the processing module is further configured to perform multiple iterative processing on the soft information generated by the propagation function of each baseband module among the propagation functions of each baseband module until the early stopping condition is met or the number of iterations reaches the maximum number of iterations, so as to obtain the bit soft information of the signal to be processed.

[0042] The signal to be processed is the input of the first propagation function in the first iteration. Each iteration includes forward message passing and backward message passing. The soft information generated by the propagation function of the baseband module corresponding to each passing node in the forward message passing and backward message passing is used as the input of the propagation function of the baseband module corresponding to the next passing node.

[0043] In one embodiment, the propagation function includes at least one of the following: Gaussian approximation message propagation (GAMP) algorithm function, confidence propagation (BP) detection algorithm function, soft modulation and demodulation algorithm, and BP decoding algorithm function.

[0044] In one embodiment, the forward messaging includes:

[0045] The GAMP algorithm function is used to process the pilot received signal or the signal feedback information from the previous cycle to generate the channel information for the current cycle. The BP detection algorithm function is used to process the channel information for the current cycle and the signal to be processed to generate the symbol soft information for the current cycle. The soft modulation and demodulation algorithm is used to demodulate the symbol soft information for the current cycle to generate the demodulated information for the current cycle. The demodulated information for the current cycle is interleaved to generate the bit soft information for the current cycle.

[0046] In one embodiment, the backward message passing includes:

[0047] The BP decoding algorithm is used to process the bit information of the current loop to generate the bit prior information of the current loop; the bit prior information of the current loop is deinterleaved to generate the deinterleaved bit prior information of the current loop; the deinterleaved bit prior information of the current loop is modulated using a soft modulation and demodulation algorithm to generate the symbol prior soft information of the current loop; and the BP detection algorithm is used to process the channel information of the current loop and the signal to be processed to generate the signal feedback information of the current loop.

[0048] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the signal processing method of the first aspect described above.

[0049] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the signal processing method of the first aspect described above.

[0050] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the signal processing method of the first aspect described above.

[0051] The aforementioned signal processing method, apparatus, device, readable storage medium, and program product first obtain the baseband common-mode algorithm model for wireless communication and the local function corresponding to each baseband module in the receiver. Second, based on the local function corresponding to each baseband module, the baseband common-mode algorithm model is converted into the propagation function of each baseband module. Third, using the propagation function corresponding to each baseband module, the signal to be processed received by the receiver is processed to obtain the bit soft information of the signal to be processed. Finally, based on the bit soft information, the original transmitted symbol corresponding to the signal to be processed is determined. Since the propagation function corresponding to each baseband module is evolved from the baseband common-mode algorithm model, the loss of useful information caused by the different message dimensions on which the algorithms of each baseband module are based can be avoided, thereby improving the system's bit error rate performance. Attached Figure Description

[0052] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0053] Figure 1 An application environment diagram of a signal processing method provided in an embodiment of this application;

[0054] Figure 2 A schematic flowchart of a signal processing method provided in an embodiment of this application;

[0055] Figure 3 A schematic diagram illustrating the principle derivation of a baseband common-mode algorithm model provided in this application embodiment;

[0056] Figure 4 A schematic diagram of a receiver based on a baseband common-mode algorithm model provided in this application embodiment;

[0057] Figure 5 A schematic diagram of a factor graph provided in an embodiment of this application;

[0058] Figure 6 A schematic flowchart illustrating another signal processing method provided in an embodiment of this application;

[0059] Figure 7 A structural block diagram of a signal processing device provided in an embodiment of this application;

[0060] Figure 8 This is an internal structural diagram of a computer device provided in an embodiment of this application. Detailed Implementation

[0061] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0062] The signal processing method provided in this application embodiment can be applied to, for example... Figure 1 In the application environment shown, transmitter 101 communicates with receiver 102. When transmitter 101 sends a signal to be processed to receiver 102, receiver 102 can first obtain the baseband common-mode algorithm model of wireless communication and the local function corresponding to each baseband module in receiver 102. Secondly, receiver 102 can convert the baseband common-mode algorithm model into the propagation function of each baseband module according to the local function corresponding to each baseband module. Thirdly, receiver 102 can use the propagation function corresponding to each baseband module to process the received signal to be processed, obtaining the bit soft information of the signal to be processed. Finally, receiver 102 can determine the original transmitted symbol corresponding to the signal to be processed based on the bit soft information.

[0063] In this system, the transmitting end 101 can be a terminal or a network device, and the receiving end 102 can also be a terminal or a network device. Terminals can be, but are not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, smart in-vehicle systems, projection devices, etc. Portable wearable devices can include smartwatches, smart bracelets, head-mounted displays, etc. Head-mounted displays can be virtual reality (VR) devices, augmented reality (AR) devices, smart glasses, etc. Network devices can include base stations.

[0064] In one exemplary embodiment, such as Figure 2 As shown, a signal processing method is provided, which is applied to... Figure 1 The following explanation will be based on the receiving end, including S201-S204:

[0065] S201. Obtain the baseband common-mode algorithm model for wireless communication and the local function corresponding to each baseband module in the receiver.

[0066] In this application, when the receiving end receives the signal to be processed, it can obtain the baseband common-mode algorithm model of wireless communication and the local function corresponding to each baseband module in the receiving end.

[0067] The aforementioned baseband common-mode algorithm model can be a general mathematical model for deriving baseband module algorithms by deeply mining the commonalities of algorithms among various baseband modules. This baseband common-mode algorithm model can be used to evolve the propagation functions of each baseband module.

[0068] The following explains how to generate the baseband common-mode algorithm model.

[0069] Figure 3 A principle derivation diagram of a baseband common-mode algorithm model provided in this application embodiment is shown below. Figure 3 As shown, when generating the baseband common-mode algorithm model, the baseband signal receiving model can be obtained first. Then, based on the Bayesian probability formula, the baseband signal receiving model is decomposed to obtain a system-level baseband algorithm model. This system-level baseband algorithm model is used to solve for the estimated value of the original transmitted symbol based on the signal to be processed. Next, the signal to be processed is decomposed into local signals corresponding to each baseband module, and the system-level baseband algorithm model is converted into a module-level baseband algorithm model based on these local signals. Third, the local signals to be processed are decomposed into the input signals of the corresponding functional nodes in the factor graph, and the module-level baseband algorithm model is converted into a node-level baseband algorithm model based on these input signals. Finally, based on the probability information transmitted along the edges between the functional nodes and variable nodes in the factor graph, the node-level baseband algorithm model is converted into a baseband common-mode algorithm model.

[0070] It should be understood that the embodiments of this application do not limit the system-level baseband algorithm model. In some embodiments, the system-level baseband algorithm model includes at least one of the following: maximum a posteriori (MAP) algorithm model, message passing algorithm model, and linear transformation algorithm model.

[0071] Accordingly, based on different types of system-level baseband algorithm models, different methods can be used to decompose the signal to be processed and the local signals to be processed. For example, if the system-level baseband algorithm model is a MAP algorithm model, the signal to be processed can be decomposed into local signals to be processed using MAP, and the local signals to be processed can be decomposed into the input signals of the corresponding functional nodes in the factor graph using MAP.

[0072] Among them, the baseband signal receiving model can be a general mathematical model corresponding to the signal to be processed received by the receiving end after the bit signal to be transmitted is processed and transmitted by each baseband module of the transmitting end device.

[0073] For example, the baseband signal receiving model can be as shown in formula (1):

[0074] y=H·u·∏k G k +n (1)

[0075] Where y is the signal to be processed received by the receiving device, H is the MIMO channel matrix, and G... k The mathematical matrix model is abstracted for the k-th baseband module, j∈{1,2,...,M}, where M is the total number of baseband modules in the transmitting device, u is the bit signal to be transmitted, and n is the system noise vector.

[0076] For example, the process of the bit signal to be transmitted being processed and transmitted by the various baseband modules of the transmitting device includes the following: the bit signal to be transmitted undergoes signal processing processes such as channel coding, interleaving, and symbol modulation; and the processed bit signal to be transmitted is transmitted to the air interface through a multiple-in multiple-out (MIMO) antenna and received by the receiving device.

[0077] In some embodiments, if the system-level baseband algorithm model is a MAP algorithm model, then based on the process of the bit signal to be transmitted being processed and transmitted by each baseband module of the transmitting device, after obtaining the baseband signal reception model, the Bayesian probability formula can be used, combined with the maximum aposterior (MAP) probability that needs to be solved for the bit signal to be transmitted, to decompose the baseband signal reception model and obtain the system-level baseband algorithm model. The system-level baseband algorithm model is used to solve for the estimated value of the original transmitted symbol based on the signal to be processed.

[0078] Among them, the MAP probability that needs to be solved for the bit signal to be transmitted can be shown in Equation (2), and the system-level baseband algorithm model can be shown in Equation (3). As shown in Equations (2) and (3), the direct solution of the bit signal to be transmitted in Equation (2) can be decomposed into the solution of the output signal of the general baseband module, the solution of the output signal of the MIMO detection module, and the solution of the prior probability in Equation (3).

[0079]

[0080] Where u is the bit signal to be transmitted, H is the MIMO channel matrix, and G... k The mathematical matrix model abstracted for the k-th baseband module. x is the estimated value of the bit signal u to be transmitted. k Let be the output signal of the k-th baseband module, k∈{1,2,...,M}, where M is the total number of baseband modules in the transmitting device; p is the posterior function, i.e., given y, H, G1, G... M The maximum a posteriori probability of the bit signal to be transmitted is calculated using p. Used to determine the output signal of a general-purpose baseband module, p(y|x) M H) is used to solve for the output signal of the MIMO detection module, p(x) M ) is used to solve for prior probabilities.

[0081] For example, starting from the received signal to be processed, the estimated value of the bit signal u to be transmitted can be obtained by deriving it from each baseband module in reverse order from the transmitter. The signal to be processed first passes through the MIMO detection module to solve for the signal, obtaining the MAP solution of the signal to be processed. Subsequently, according to Find the MAP solution for the (M-1)th module. By analogy, based on the general formula for solving the output signal of the baseband module, the MAP solution is obtained module by module until the estimated value of the bit signal u to be transmitted is obtained.

[0082] In some embodiments, the signal to be processed can be split into local signals to be processed corresponding to each baseband module, and the system-level baseband algorithm model can be converted into a module-level baseband algorithm model based on the local signals to be processed corresponding to each baseband module.

[0083] Among them, the local signal to be processed can be the input signal of the corresponding baseband module.

[0084] It should be understood that the processing of the signal received by the receiver can be broken down into the processing procedures of each baseband module, which are uniform in form. Correspondingly, the signal to be processed can also be broken down into the local signals to be processed corresponding to each baseband module.

[0085] It should be understood that the embodiments of this application do not limit how the signal to be processed is decomposed into local signals to be processed corresponding to each baseband module. In some embodiments, it can be done based on the type of system-level baseband algorithm model.

[0086] For example, if the system-level baseband algorithm model is the MAP algorithm model, then in formula (3), for each baseband module, it can be determined based on its output signal x. k and G k Find the local signal to be processed (i.e., the input signal x). k-1 The MAP solution of the signal to be processed can be obtained. Based on this, the signal estimate of the signal to be processed can be decomposed into the maximum posterior probability (MAP) of the local signal to be processed corresponding to each baseband module, thereby transforming the system-level baseband algorithm model into a module-level baseband algorithm model.

[0087] For example, the module-level baseband algorithm model can be as shown in formula (4):

[0088]

[0089] Where, x k Let B be the output signal of the k-th baseband module, k∈{1,2,...,M}, and let B be the local signal to be processed (input signal) x. k-1 The value space of G k The mathematical matrix model abstracted for the k-th baseband module can be used as a constraint matrix, p(x k |x k-1 G k p(x) represents the likelihood probability. k-1 ) represents the priori probability of the input signal of a general baseband module.

[0090] It should be noted that among the multiple baseband modules in the receiver, the output signal of the preceding baseband module can be used as the local signal to be processed (input signal) of the following baseband module; that is, the output signal x of the (k-1)th baseband module... k-1 It can be used as the local signal to be processed (input signal) x of the k-th baseband module. k-1 .

[0091] It should be understood that the multiplication of the likelihood probability term and the prior probability term in the above formula evaluates the output signal of the k-th baseband module, given that the output signal is x. k At that time, the different local signals to be processed (input signals) x of the k-th baseband module k-1 The probability of occurrence. Furthermore, due to the difference in the mathematical matrix representation between the MIMO detection module and the general baseband module, the signal solution process for the MIMO detection module in the system-level baseband algorithm model is separated, but the signal solution for the MIMO detection module can still follow the form of the module-level baseband algorithm model.

[0092] In some embodiments, a message passing algorithm can be used to solve the problem during the processing of the signal to be processed. Each baseband module can be generally represented by a baseband common-mode algorithm model, and the local signal to be processed x of the relevant baseband module is provided. k-1 The solution method involves using a factor graph and a message-passing algorithm to solve for the local signal x to be processed. k-1 The marginal distribution probability.

[0093] The factor graph includes variable nodes and function nodes. Variable nodes represent the local signals to be processed by the corresponding baseband module, while function nodes represent the constraints imposed by the corresponding baseband module on the local signals to be processed. Variable nodes and function nodes are connected by edges.

[0094] It should be understood that the embodiments of this application do not limit how the local signal to be processed is decomposed into the input signals of the corresponding functional nodes in the factor graph. In some embodiments, it can be done based on the type of system-level baseband algorithm model.

[0095] For example, if the system-level baseband algorithm model is the MAP algorithm model, in the factor graph, the functional nodes correspond to the constraint matrix G. k The row vectors, where the variable nodes correspond to the constraint matrix G. k The column vectors, function nodes also provide likelihood probabilities of corresponding constraints to variable nodes, and variable nodes also provide prior probabilities of the variables to be solved to function nodes. For example, if the constraint matrix G... k The row vector formed by the elements of the i-th row is g. i This corresponds to the i-th functional node. Based on the constraint matrix G k The maximum a posteriori probability solution is decomposed into g-based methods. i The formula for the maximum a posteriori probability of row vectors. Based on this, the module-level baseband algorithm model can be converted into a node-level baseband algorithm model. The node-level baseband algorithm model can be expressed as formula (5):

[0096]

[0097] Where Θ is the input signal x of the functional node. j The value space of y i Let be the i-th element of the output signal of baseband module k, and let x be the input signal of the functional node to be solved. The message passing algorithm iteratively updates the likelihood probability p(y) of the input signal x of the functional node to be solved. i |x,g i The maximum a posteriori probability of the bit signal u to be transmitted is solved by using the prior probability P(x) and the prior probability P(x).

[0098] In some embodiments, the probability information transmitted along the edge from the functional node to the variable node and the probability information transmitted along the edge from the variable node to the functional node can be determined respectively. Then, based on the probability information transmitted along the edge from the functional node to the variable node and the probability information transmitted along the edge from the variable node to the functional node, the node-level baseband algorithm model can be converted into a baseband common-mode algorithm model.

[0099] For example, the probability information passed from the i-th functional node to the j-th variable node along the edge in the factor graph can be defined as r. ij (μ k ), μ k Let r be the input signal of the functional node to be solved. According to the calculation rules of the sum-product algorithm (SPA) of the factor graph, r... ij (μ kIt can be determined by formula (6):

[0100]

[0101] Where i is a function node, j is a variable node, and F i (x) represents the local function of the function node, q li This represents the prior probability of the signal to be processed.

[0102] For example, the probability information passed from the j-th variable node to the i-th functional node along the edge in the factor graph can be defined as q. ji (μ k ), μ k q represents the input signal of the functional node to be solved. According to the calculation rules of the sum-product algorithm (SPA) of the factor graph, q... ji (μ k It can be determined by formula (7):

[0103] q ji (μ k )=ξ ji ×∏ l≠i r lj (μ k (7)

[0104] Where i is a functional node, j is a variable node, and ξ ji The scaling factor is used to accelerate algorithm convergence.

[0105] For example, to facilitate hardware implementation, the log-likelihood ratio (LLR) can be used to represent the various probability information. That is, R... ij (μ k ) represents the probability information r that is passed from the i-th functional node to the j-th variable node along the edge. ij (μ k The LLR form of Q ji (μ k ) is the probability message q sent by the h-th variable node to the i-th functional node. ji (μ k The LLR form of ). R ij (μ k ) and Q ji (μ k It can be shown in formula (8):

[0106]

[0107] Where, μ kBoth μ0 and μ0 are signals to be processed x j The value of x. It should be understood that through x... j Take μ k The probability divided by x j By taking μ0 and then calculating the logarithm, the log-likelihood ratio can be determined.

[0108] For example, the node-level baseband algorithm model based on formula (8) can be converted into a baseband common-mode algorithm model, which can be as follows (9):

[0109]

[0110] Among them, R ij (μ k ) represents the probability information r that is passed from the i-th functional node to the j-th variable node along the edge. ij (μ k The LLR form of Q ji (μ k The probability message q sent by the j-th variable node to the i-th functional node. ji (μ k The LLR form of ) x k Let μ be the output signal of the k-th baseband module, k∈{1,2,...,M}. k The input signal is the function node to be solved.

[0111] In this application, the baseband common mode algorithm model is implemented by combining a general mathematical model and a flexible and configurable local function derivation. The baseband common mode algorithm has a unified underlying hardware architecture, which can achieve multi-module hardware architecture reuse through hardware folding, configuration and other methods, thereby improving hardware design efficiency.

[0112] It should be understood that the embodiments of this application do not limit the local functional functions corresponding to the baseband module. In some embodiments, the baseband module may include a channel estimation module, a MIMO detection module, a modulation / demodulation module, an interleaving code module, and a channel decoding module.

[0113] The channel decoding module may include a low-density parity-check (LDPC) decoding module and a polar decoding module. The interleaving code module may be an interleaved code division multiple access (IDMA) detection module.

[0114] In some embodiments, different baseband modules may have different local function functions, which may be pre-configured based on the different functions of each baseband module.

[0115] S202. Based on the local function corresponding to each baseband module, convert the baseband common mode algorithm model into the propagation function of each baseband module.

[0116] In this step, after obtaining the baseband common-mode algorithm model for wireless communication and the local function corresponding to each baseband module in the receiver, the baseband common-mode algorithm model can be converted into the propagation function of each baseband module according to the local function corresponding to each baseband module.

[0117] The propagation function includes at least one of the following: Generalized Approximate Message Passing (GAMP) algorithm function, Belief Propagation (BP) detection algorithm function, soft modulation and demodulation algorithm, and BP decoding algorithm function.

[0118] In some embodiments, the baseband common-mode algorithm model includes a function to be evolved. Accordingly, the function to be evolved in the baseband common-mode algorithm model can be replaced with the local function corresponding to each baseband module, so as to convert the baseband common-mode algorithm model into the propagation function of each baseband module.

[0119] For each baseband module, its corresponding local function can be determined by the specific characteristics of the corresponding factor graph. These characteristics can include sparsity, signal value space, etc.

[0120] The evolution of the propagation function for different baseband modules will be explained separately below.

[0121] In some embodiments, the function to be evolved in the common-mode algorithm model can be replaced with the local function corresponding to the channel estimation module, thereby obtaining the propagation function of the channel estimation module.

[0122] For example, the local function corresponding to the channel estimation module can be as shown in formula (10), using the local function F corresponding to the channel estimation module. i (h), replace the function F to be evolved in the baseband common-mode algorithm model shown in formula (9). i (x), thus evolving GAMP, the propagation function of the channel estimation module is shown in equation (11):

[0123]

[0124] Where h is the output signal of the channel estimation module corresponding to the channel vector to be estimated; y is the signal to be processed obtained at the receiver, corresponding to the input signal of the channel estimation module; σ 2 R represents the noise variance. ij (μk ) represents the probability information r that is passed from the i-th functional node to the j-th variable node along the edge. ij (μ k The LLR form of Q ji (μ k The probability message q sent by the j-th variable node to the i-th functional node. ji (μ k The LLR form of ) x k Let μ be the output signal of the k-th baseband module, k∈{1,2,...,M}. k The signal to be processed is x j The value of .

[0125] For example, the input signal can be iterated multiple times through the propagation function of the channel estimation module. When the maximum number of iterations is reached, GAMP stops iterating and outputs channel information (probabilistic message).

[0126] For example, the local function corresponding to the MIMO detection module can be as shown in formula (12), using the local function F corresponding to the MIMO detection module. i (h), replace the function F to be evolved in the baseband common-mode algorithm model shown in formula (9). i (x), thus evolving the BP detection algorithm, the propagation function of the MIMO detection module is shown in formula (13):

[0127]

[0128] Where x is the input signal of the functional node to be solved, and in formula (13) for the functional node being the MIMO detection module, x can be the symbol vector to be estimated; y i h is the i-th element of the output signal of baseband module k, corresponding to the output signal of the MIMO detection module; i Let σ be the i-th row vector of the channel matrix. 2 R represents the noise variance. ij (μ k ) represents the probability information r that is passed from the i-th functional node to the j-th variable node along the edge. ij (μ k The LLR form of Q ji (μ k The probability message q sent by the j-th variable node to the i-th functional node. ji (μ k The LLR form of ) x k Let μ be the output signal of the k-th baseband module, k∈{1,2,...,M}. k The signal to be processed is x j The value of .

[0129] For example, the propagation function of the MIMO detection module can be used to iterate the local signal to be processed (input signal) corresponding to the MIMO detection module multiple times. When the maximum number of iterations is reached, the BP detection algorithm terminates the message update and outputs symbol soft information (symbol LLR soft information). Its calculation formula can be shown in formula (14):

[0130]

[0131] Where, γ j (μ k ) represents the input signal x of the functional node to be solved. j The value of μ k LLR soft information, N r The number of receiving antennas represents the number of constraints in the mathematical model of the MIMO detection module.

[0132] For example, the local function corresponding to the modulation / demodulation module can be as shown in formula (15). The local function corresponding to the modulation / demodulation module can substantially characterize the mapping relationship between modulation symbols and bits. Using the local function F corresponding to the modulation / demodulation module... i (x), replace the function F to be evolved in the baseband common-mode algorithm model shown in formula (9). i (x), thereby evolving the propagation function of the modulation / demodulation module, and the resulting propagation function of the modulation / demodulation module is shown in formula (16):

[0133] F i (h)=δ k-m (15)

[0134]

[0135] Where, δ k-m For μ k c, as the modulation symbol and the modulated bit sequence m The mapping relationship indicates that if the modulation symbol μ k and the modulated bit sequence c m If the mapping relationship is satisfied, the value is 1; otherwise, it is 0. j (n) represents the LLR soft information of the nth bit of the j-th modulation symbol, c n For the bit sequence c that satisfies the mapping relationship m The nth bit in.

[0136] For example, if the channel decoding module is an LDPC decoding module, the local function corresponding to the channel decoding module can be as shown in formula (17). The local function F corresponding to the LDPC decoding module is used.i (h), replace the function F to be evolved in the baseband common-mode algorithm model shown in formula (9). i (x), thus evolving the BP decoding algorithm, the resulting BP decoding algorithm for the LDPC decoding module is shown in formula (18). Assuming that the LDPC code is based on bit field expansion and the message transmitted in the factor graph is a 0 / 1 type bit LLR, the propagation function of the LDPC decoding module can be further simplified to formula (19).

[0137]

[0138] Where x is the input signal of the functional node to be solved, in formula (19) for a decoding module, x can be an LDPC coded bit, i.e., the output signal of the channel decoding module; H(i,:) is the i-th row vector of the LDPC code parity matrix; I j (μ k ) represents the initialization prior information for the transmitted signal, i.e., the input signal of the channel decoding module, I j The LLR soft information is the bit input to the decoder.

[0139] For example, through multiple iterations of the propagation function of the LDPC decoding module, the BP decoding algorithm of the LDPC decoding module terminates the message update and outputs the signal feedback information of each bit. Its calculation formula can be shown as formula (20):

[0140]

[0141] Where, γ j Let LLR be the 0 / 1 type bit of the j-th transmitted bit. T is the number of check equations for the LDPC code, i.e., the number of constraints in the mathematical model of the channel decoding module.

[0142] It should be noted that the embodiments of this application do not limit the local function functions and the corresponding propagation functions of each baseband module. For example, Table 1 records the local function functions and the corresponding propagation functions of various baseband modules.

[0143] Table 1

[0144]

[0145] S203. Using the propagation functions corresponding to each baseband module, the signal to be processed received by the receiving end is processed to obtain the bit soft information of the signal to be processed.

[0146] In this step, after the baseband common-mode algorithm model is converted into the propagation function of each baseband module according to the local function corresponding to each baseband module, the signal to be processed received by the receiving end can be processed using the propagation function corresponding to each baseband module to obtain the bit soft information of the signal to be processed.

[0147] It should be understood that the embodiments of this application do not limit how the propagation functions corresponding to each baseband module are used to process the signal to be processed received by the receiving end. In some embodiments, the soft information generated by the propagation functions of each baseband module can be processed in multiple loop iterations between the propagation functions of each baseband module until the early stopping condition is met or the number of loops reaches the maximum number of iterations, so as to obtain the bit soft information of the signal to be processed.

[0148] The signal to be processed is the input of the first propagation function in the first loop iteration. Each loop iteration includes forward message passing and backward message passing. The soft information generated by the propagation function of the baseband module corresponding to each passing node in the forward and backward message passing is used as the input of the propagation function of the baseband module corresponding to the next passing node.

[0149] For example, the early stopping condition can be a Cyclic Redundancy Check (CRC) condition, and the maximum number of iterations can be set according to the actual situation, such as 10, 20, 50, etc.

[0150] In some embodiments, during forward message passing, the GAMP algorithm function can first be used to process the pilot received signal or the signal feedback information from the previous loop to generate the channel information for the current loop. Next, the BP detection algorithm function is used to process the channel information and the signal to be processed in the current loop to generate the symbol soft information for the current loop. Then, a soft modulation / demodulation algorithm is used to demodulate the symbol soft information for the current loop to generate the demodulated information for the current loop. Finally, the demodulated information for the current loop is interleaved to generate the bit soft information for the current loop.

[0151] For example, Figure 4 A schematic diagram of a receiver based on a baseband common-mode algorithm model is provided for an embodiment of this application, as shown below. Figure 4 As shown, the baseband module in the receiver includes a channel estimation module, a MIMO detection module, a modulation / demodulation module, an interleaver module, and a channel decoding module. p y represents the pilot signal received, and y represents the signal to be processed received by the receiver. The forward message passing includes four transmission steps.

[0152] In the first forward pass step, the channel estimation module receives the pilot reception signal y transmitted by the transmitter.p Alternatively, upon receiving the signal feedback information A(x1) from the previous cycle, the channel estimation module iteratively processes it using the GAMP algorithm to output the channel information E(x1) for the current cycle. In the second forward pass step, the MIMO detection module uses the BP detection algorithm function to process the channel information E(x1) and the signal to be processed y for the current cycle, outputting the symbol soft information E(x2) for the current cycle. In the third forward pass step, the modulation / demodulation module uses a soft modulation / demodulation algorithm to demodulate the symbol soft information E(x2) for the current cycle, generating the demodulated information E(x3) for the current cycle. In the fourth forward pass step, the interleaver module interleaves the demodulated information E(x3) for the current cycle, generating the bit soft information E(x4) for the current cycle.

[0153] It should be understood that the channel information E(x1), symbol soft information E(x2), demodulation information E(x3), and bit soft information E(x4) mentioned above can all be soft information generated by the propagation function of each baseband module.

[0154] For example, the channel information E(x1) mentioned above can be the output signal h of the channel estimation module corresponding to the channel vector to be estimated in formula (11), and the symbol soft information E(x2) can be the LLR soft information γ in formula (13). j (μ k The demodulated information E(x3) can be the bit soft information R. j (n).

[0155] In some embodiments, during backward message passing, the bit information of the current loop can first be processed using a BP decoding algorithm function to generate the bit prior information of the current loop. Secondly, the bit prior information of the current loop can be deinterleaved to generate deinterleaved bit prior information of the current loop. Thirdly, a soft modulation / demodulation algorithm can be used to modulate the deinterleaved bit prior information of the current loop to generate symbol prior soft information of the current loop. Finally, a BP detection algorithm function can be used to process the channel information and the signal to be processed in the current loop to generate the signal feedback information of the current loop.

[0156] Continue to refer to Figure 4 The backward message passing also includes four passing steps.

[0157] In the first backward pass step, the channel decoding module, based on the current cycle's bit soft information R(x4), iterates using the BP decoding algorithm and feeds back the current cycle's bit prior information A(x4) to the interleaver. In the second backward pass step, the interleaver module deinterleaves the current cycle's bit prior information A(x4) to generate the current cycle's deinterleaved bit prior information A(x3). In the third backward pass step, the current cycle's deinterleaved bit prior information A(x3) is modulated by the modulation / demodulation module to generate the current cycle's symbol prior soft information A(x2). In the fourth backward pass step, the MIMO detection module, based on the current cycle's symbol prior soft information A(x2), iterates using the BP detection algorithm and feeds back the current cycle's signal feedback information A(x1) to the channel estimation module. The current cycle's signal feedback information A(x1) can be used in the next cycle to iteratively update the channel information E(x1) using the GAMP algorithm.

[0158] It should be understood that the aforementioned bit prior information A(x4), deinterleaved bit prior information A(x3), deinterleaved bit prior information A(x3), and signal feedback information A(x1) can all be soft information generated by the propagation function of each baseband module. Among them, bit prior information A(v4) can be the LDPC encoded bit x in formula (19).

[0159] For example, Figure 5 This is a schematic diagram of a factor graph provided in an embodiment of this application, such as... Figure 5 As shown, after the receiving device receives the signal y to be processed, it can first be processed by the functional nodes f corresponding to the channel estimation module and the MIMO module. Functional nodes f process the signal y to be processed, obtaining variable nodes h and s respectively. Subsequently, the modulation / demodulation module processes the corresponding variable node s to obtain variable node c. Finally, after passing through the interleaver, the corresponding variable node v is obtained, which is then processed by the functional node corresponding to the channel decoding module.

[0160] It should be noted that the signal to be processed may include multiple Orthogonal Frequency Division Multiplexing (OFDM) symbols, each transmitting different information on a subcarrier. In this application, channel estimation, MOMO detection, modulation, or demodulation can be performed on multiple OFDM symbols in parallel to obtain soft information of the multiple OFDM symbols processed in parallel. The soft information of the multiple OFDM symbols processed in parallel is then sent to an interleaver for interleaving and subsequent channel decoding.

[0161] S204. Determine the original transmitted symbol corresponding to the signal to be processed based on the bit soft information.

[0162] In this step, after processing the signal to be processed received by the receiving end using the propagation function corresponding to each baseband module and obtaining the bit soft information of the signal to be processed, the original transmitted symbol corresponding to the signal to be processed can be determined based on the bit soft information.

[0163] It should be understood that the embodiments of this application do not limit how to determine the original transmitted symbol corresponding to the signal to be processed based on the bit soft information. In some embodiments, hard decision can be made on the bit soft information, so as to recover the original transmitted symbol after recovery.

[0164] It should be understood that hard decision is a process of simplifying bit soft information, that is, converting each bit soft information into the closest binary value. For example, if the bit soft information is greater than 0, the hard decision result is 1; if the bit soft information is less than 0, the hard decision result is 0. Recovering the original transmitted symbol through hard decision is simple, direct, and highly efficient.

[0165] In this application, after the early stopping condition is met or the number of iterations reaches the maximum, bit soft information is obtained, and then hard decision is performed to recover the original transmitted symbol. Compared with directly using hard decision, iterative decoding combined with bit soft information can provide better bit error rate performance when the channel conditions are poor.

[0166] The signal processing method provided in this application first obtains the baseband common-mode algorithm model for wireless communication and the local function corresponding to each baseband module in the receiver. Second, based on the local function corresponding to each baseband module, the baseband common-mode algorithm model is converted into the propagation function of each baseband module. Third, the signal to be processed received by the receiver is processed using the propagation function corresponding to each baseband module to obtain the bit soft information of the signal to be processed. Finally, based on the bit soft information, the original transmitted symbol corresponding to the signal to be processed is determined. Since the propagation function corresponding to each baseband module is generated through the baseband common-mode algorithm model, the loss of useful information caused by the different message dimensions on which the algorithms of each baseband module are based can be avoided, thereby improving the system's bit error rate performance.

[0167] The following explains how to generate the baseband common-mode algorithm model. Figure 6 A flowchart illustrating another signal processing method provided in this application embodiment is shown below. Figure 6 As shown, this includes S301-S309:

[0168] S301. Obtain the baseband signal receiving model.

[0169] Among them, the baseband signal receiving model can be a general mathematical model corresponding to the signal to be processed received by the receiving end after the bit signal to be transmitted is processed and transmitted by each baseband module of the transmitting end device.

[0170] S302. Based on the Bayesian probability formula, the baseband signal receiving model is decomposed to obtain the system-level baseband algorithm model.

[0171] Among them, the system-level baseband algorithm model is used to solve for the estimated value of the original transmitted symbol based on the signal to be processed.

[0172] S303. The signal to be processed is split into local signals to be processed corresponding to each baseband module, and the system-level baseband algorithm model is converted into a module-level baseband algorithm model based on the local signals to be processed corresponding to each baseband module.

[0173] S304. Decompose the local signal to be processed into the input signals of the corresponding functional nodes in the factor graph, and convert the module-level baseband algorithm model into a node-level baseband algorithm model according to the input signals of the corresponding functional nodes in the factor graph.

[0174] S305. Based on the probability information transmitted along the edges between the functional nodes in the factor graph and the variable nodes in the factor graph, the node-level baseband algorithm model is converted into a baseband common-mode algorithm model.

[0175] Among them, variable nodes are used to represent the local signal to be processed by the corresponding baseband module, and functional nodes are used to represent the constraints of the corresponding baseband module on the local signal to be processed; the system-level baseband algorithm model includes at least one of the following: maximum a posteriori probability (MAP) algorithm model, message passing algorithm model, and linear transformation algorithm model.

[0176] S306. Obtain the baseband common-mode algorithm model for wireless communication and the local function corresponding to each baseband module in the receiver.

[0177] S307. Based on the local function corresponding to each baseband module, convert the baseband common mode algorithm model into the propagation function of each baseband module.

[0178] S308. Using the propagation functions corresponding to each baseband module, the signal to be processed received by the receiving end is processed to obtain the bit soft information of the signal to be processed.

[0179] S309. Determine the original transmitted symbol corresponding to the signal to be processed based on the bit soft information.

[0180] The signal processing method provided in this application first obtains the baseband common-mode algorithm model for wireless communication and the local function corresponding to each baseband module in the receiver. Second, based on the local function corresponding to each baseband module, the baseband common-mode algorithm model is converted into the propagation function of each baseband module. Third, the signal to be processed received by the receiver is processed using the propagation function corresponding to each baseband module to obtain the bit soft information of the signal to be processed. Finally, based on the bit soft information, the original transmitted symbol corresponding to the signal to be processed is determined. Since the propagation function corresponding to each baseband module is generated through the baseband common-mode algorithm model, the loss of useful information caused by the different message dimensions on which the algorithms of each baseband module are based can be avoided, thereby improving the system's bit error rate performance.

[0181] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0182] Based on the same inventive concept, this application also provides a signal processing apparatus for implementing the signal processing method described above. The solution provided by this apparatus is similar to the implementation scheme described in the above method; therefore, the specific limitations in one or more signal processing apparatus embodiments provided below can be found in the limitations of the signal processing method above, and will not be repeated here.

[0183] In one exemplary embodiment, such as Figure 7 As shown, a signal processing device 400 is provided, including: an acquisition module 401, a conversion module 402, and a processing module 403, wherein:

[0184] Module 401 is used to acquire the baseband common-mode algorithm model for wireless communication and the local function corresponding to each baseband module in the receiver.

[0185] The conversion module 402 is used to convert the baseband common mode algorithm model into the propagation function of each baseband module according to the local function corresponding to each baseband module.

[0186] The processing module 403 is used to process the signal to be processed received by the receiving end using the propagation function corresponding to each baseband module to obtain the bit soft information of the signal to be processed; and to determine the original transmitted symbol corresponding to the signal to be processed based on the bit soft information.

[0187] In one embodiment, the signal processing device 400 further includes:

[0188] The model generation module 401 is used to obtain the baseband signal receiving model; according to the Bayesian probability formula, the baseband signal receiving model is decomposed to obtain a system-level baseband algorithm model, which is used to solve for the estimated value of the original transmitted symbol based on the signal to be processed; the signal to be processed is decomposed into local signals to be processed corresponding to each baseband module, and the system-level baseband algorithm model is converted into a module-level baseband algorithm model based on the local signals to be processed corresponding to each baseband module; the local signals to be processed are decomposed into the input signals of the corresponding functional nodes in the factor graph, and the module-level baseband algorithm model is converted into a node-level baseband algorithm model based on the input signals of the corresponding functional nodes in the factor graph; based on the probability information transmitted along the edges between the functional nodes and the variable nodes in the factor graph, the node-level baseband algorithm model is converted into a baseband common-mode algorithm model.

[0189] Among them, variable nodes are used to represent the local signal to be processed by the corresponding baseband module, and functional nodes are used to represent the constraints of the corresponding baseband module on the local signal to be processed; the system-level baseband algorithm model includes at least one of the following: maximum a posteriori probability (MAP) algorithm model, message passing algorithm model, and linear transformation algorithm model.

[0190] In one embodiment, the baseband common-mode algorithm model includes a function to be evolved; the conversion module 402 is further configured to replace the function to be evolved in the baseband common-mode algorithm model with the local function corresponding to each baseband module, so as to convert the baseband common-mode algorithm model into the propagation function of each baseband module.

[0191] In one embodiment, the processing module 403 is further configured to perform multiple iterative processing on the soft information generated by the propagation function of each baseband module between the propagation functions of each baseband module until the early stopping condition is met or the number of iterations reaches the maximum number of iterations, so as to obtain the bit soft information of the signal to be processed.

[0192] The signal to be processed is the input of the first propagation function in the first loop iteration. Each loop iteration includes forward message passing and backward message passing. The soft information generated by the propagation function of the baseband module corresponding to each passing node in the forward and backward message passing is used as the input of the propagation function of the baseband module corresponding to the next passing node.

[0193] In one embodiment, the propagation function includes at least one of the following: Gaussian approximation message propagation (GAMP) algorithm function, confidence propagation (BP) detection algorithm function, soft modulation and demodulation algorithm, and BP decoding algorithm function.

[0194] In one embodiment, forward messaging includes:

[0195] The GAMP algorithm function is used to process the pilot received signal or the signal feedback information from the previous loop to generate the channel information for the current loop. The BP detection algorithm function is used to process the channel information and the signal to be processed in the current loop to generate the symbol soft information for the current loop. The soft modulation and demodulation algorithm is used to demodulate the symbol soft information for the current loop to generate the demodulated information for the current loop. The demodulated information for the current loop is interleaved to generate the bit soft information for the current loop.

[0196] In one embodiment, backward message passing includes:

[0197] The BP decoding algorithm is used to process the bit information of the current loop to generate the bit prior information of the current loop; the bit prior information of the current loop is deinterleaved to generate the deinterleaved bit prior information of the current loop; the soft modulation and demodulation algorithm is used to modulate the deinterleaved bit prior information of the current loop to generate the symbol prior soft information of the current loop; the BP detection algorithm is used to process the channel information and the signal to be processed in the current loop to generate the signal feedback information of the current loop.

[0198] Each module in the aforementioned signal processing device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the operations corresponding to each module.

[0199] In one exemplary embodiment, a computer device is provided, which may be a terminal or a network device, and its internal structure diagram may be as follows. Figure 8As shown, the computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When executed by the processor, the computer program implements a signal processing method.

[0200] Those skilled in the art will understand that Figure 8 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0201] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the signal processing method described above.

[0202] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the above-described signal processing method.

[0203] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the signal processing method described above.

[0204] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0205] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0206] The above embodiments are merely illustrative of several implementation methods of this application, and their descriptions are relatively specific and detailed. However, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A signal processing method, characterized in that, The method includes: The method involves obtaining the baseband common-mode algorithm model for wireless communication and the local function corresponding to each baseband module in the receiver. The baseband common-mode algorithm model is a general mathematical model of the baseband module algorithm derived by mining the commonalities of the algorithms of each baseband module. This model is used to evolve the propagation function of each baseband module. Different baseband modules have different local function functions, which are pre-configured based on the different functions of each baseband module. Based on the local function corresponding to each baseband module, the baseband common mode algorithm model is converted into the propagation function of each baseband module respectively; The signal to be processed received by the receiving end is processed using the propagation function corresponding to each baseband module to obtain the bit soft information of the signal to be processed. Based on the bit soft information, determine the original transmission symbol corresponding to the signal to be processed.

2. The method according to claim 1, characterized in that, Before obtaining the baseband common-mode algorithm model for wireless communication and the local function corresponding to each baseband module in the receiver, the method further includes: Obtain the baseband signal receiving model; According to the Bayesian probability formula, the baseband signal receiving model is decomposed to obtain a system-level baseband algorithm model. The system-level baseband algorithm model is used to solve for the estimated value of the original transmitted symbol based on the signal to be processed. The signal to be processed is split into local signals to be processed corresponding to each baseband module, and the system-level baseband algorithm model is converted into a module-level baseband algorithm model based on the local signals to be processed corresponding to each baseband module. The local signal to be processed is split into the input signals of the corresponding functional nodes in the factor graph, and the module-level baseband algorithm model is converted into a node-level baseband algorithm model according to the input signals of the corresponding functional nodes in the factor graph. Based on the probability information transmitted along the edges between the functional nodes and the variable nodes in the factor graph, the node-level baseband algorithm model is converted into the baseband common-mode algorithm model. The variable nodes are used to characterize the local signal to be processed by the corresponding baseband module, and the functional nodes are used to characterize the constraints of the corresponding baseband module on the local signal to be processed. The system-level baseband algorithm model includes at least one of the following: maximum a posteriori probability (MAP) algorithm model, message passing algorithm model, and linear transformation algorithm model.

3. The method according to claim 1, characterized in that, The baseband common-mode algorithm model includes a function to be evolved; the step of converting the baseband common-mode algorithm model into the propagation function of each baseband module according to the local function corresponding to each baseband module includes: The function to be evolved in the baseband common mode algorithm model is replaced with the local function corresponding to each baseband module, so as to convert the baseband common mode algorithm model into the propagation function of each baseband module.

4. The method according to any one of claims 1-3, characterized in that, The process of using the propagation functions of each baseband module to process the signal to be processed received by the receiving end to obtain the target bit soft information of the signal to be processed includes: The soft information generated by the propagation function of each baseband module is processed through multiple loop iterations between the propagation functions of each baseband module until the early stopping condition is met or the number of loops reaches the maximum number of iterations, so as to obtain the bit soft information of the signal to be processed. The signal to be processed is the input of the first propagation function in the first iteration. Each iteration includes forward message passing and backward message passing. The soft information generated by the propagation function of the baseband module corresponding to each passing node in the forward message passing and backward message passing is used as the input of the propagation function of the baseband module corresponding to the next passing node.

5. The method according to claim 4, characterized in that, The propagation function includes at least one of the following: Gaussian approximation message propagation (GAMP) algorithm function, confidence propagation (BP) detection algorithm function, soft modulation and demodulation algorithm, and BP decoding algorithm function.

6. The method according to claim 5, characterized in that, The forward message passing includes: The GAMP algorithm function is used to process the pilot received signal or the signal feedback information from the previous loop to generate the channel information for the current loop. The BP detection algorithm function is used to process the channel information of the current loop and the signal to be processed to generate symbol soft information for the current loop. A soft modulation and demodulation algorithm is used to demodulate the symbol soft information of the current loop to generate the demodulated information of the current loop. The demodulated information of the current loop is interleaved to generate the bit soft information of the current loop.

7. The method according to claim 5 or 6, characterized in that, The backward message passing includes: The BP decoding algorithm function is used to process the bit information of the current loop and generate the bit prior information of the current loop. The bit prior information of the current loop is deinterleaved to generate deinterleaved bit prior information for the current loop; A soft modulation and demodulation algorithm is used to modulate the deinterleaved bit prior information of the current cycle to generate symbol prior soft information of the current cycle. The BP detection algorithm function is used to process the channel information of the current loop and the signal to be processed to generate the signal feedback information of the current loop.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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