SCMA-based detection and decoding method and related equipment thereof

Abstract

The invention discloses an SCMA-based detection and decoding method and related equipment thereof, and relates to the technical field of communication, and the SCMA-based detection and decoding method comprises the steps: receiving symbol sequence data corresponding to a plurality of users; performing iterative decoding and detection on the symbol sequence data through a preset expected propagation algorithm and a soft output decoder, and outputting bit sequence information after a preset stop condition is reached, the symbol sequence data comprising a plurality of variable nodes and a plurality of function nodes, the variable nodes and the function nodes are iteratively updated based on prior information. According to the invention, the bit error rate in the data transmission process is reduced.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and in particular to a detection and decoding method based on SCMA and related equipment. Background Art

[0002] In satellite-to-ground communications, sparse code multiple access (SCMA) is one of the candidate multiple access technologies that are expected to be applied in satellite communications. In related technologies, a joint detection and decoding (JDD) scheme is proposed to combine SCMA detection and low-density parity check (LDPC) code decoding in a joint sparse graph. This approach is based on the assumption that multi-user signals are fully synchronized at the receiving end. However, in satellite-to-ground communications, if strict synchronization is to be achieved, frequent interactions between large-scale satellites and massive terminals cannot be avoided, which will lead to huge signaling overhead and additional power consumption, resulting in high latency and actual high bit error rates.

[0003] The above contents are only used to assist in understanding the technical solution of the present application and do not constitute an admission that the above contents are prior art. Summary of the invention

[0004] The main purpose of this application is to provide a detection and decoding method based on SCMA and related equipment, aiming to solve the technical problems of high delay and high actual bit error rate in satellite-to-earth communication in related technologies.

[0005] To achieve the above objectives, the present application proposes a detection and decoding based on SCMA, and the detection and decoding based on SCMA includes:

[0006] Receiving symbol sequence data corresponding to multiple users;

[0007] The symbol sequence data is iteratively decoded and detected by a preset expected propagation algorithm and a soft output decoder, and bit sequence information is output after a preset stop condition is reached, wherein the symbol sequence data includes a plurality of variable nodes and a plurality of function nodes, and the variable nodes and the function nodes are iteratively updated based on prior information.

[0008] In one embodiment, the step of iteratively decoding and detecting the symbol sequence data by using a preset expectation propagation algorithm and a soft output decoder, and outputting the bit sequence information after reaching a preset stop condition, comprises:

[0009] By using a preset expectation propagation algorithm, the symbol sequence data is iteratively updated to obtain an external information log-likelihood ratio;

[0010] Inputting the external information log-likelihood ratio into a soft output decoder, and decoding the external information log-likelihood ratio based on the soft output decoder to obtain soft information;

[0011] The soft information is iteratively exchanged between a detector corresponding to a preset expected propagation algorithm and a soft output decoder until a preset stop condition is reached, and then bit sequence information is output.

[0012] In one embodiment, before the step of iteratively updating the symbol sequence data by using a preset expectation propagation algorithm to obtain the external information log-likelihood ratio, the step further includes:

[0013] Constructing an expansion factor graph based on the asynchronous interference relationship between the symbol sequence data;

[0014] The extended factor graph includes a plurality of sub-factor graphs corresponding to the number of transmissions, each of the sub-factor graphs includes a plurality of prior nodes, variable nodes and function nodes, the prior nodes of the extended factor graph represent the prior probability of the codeword, the variable nodes represent the transmitted codeword, and the function nodes represent the received discrete symbols.

[0015] In one embodiment, the step of iteratively updating the symbol sequence data by a preset expectation propagation algorithm to obtain the external information log-likelihood ratio includes:

[0016] Based on the prior nodes, the variable nodes and the function nodes, determining the posterior probability corresponding to the symbol sequence data, the first transfer information transmitted from the variable node to the function node and the second transfer information transmitted from the function node to the variable node;

[0017] Converting the first transfer information and / or the second transfer information into Gaussian distribution information to obtain first distribution information and second distribution information;

[0018] Iteratively calculating the first distribution information and the second distribution information respectively to obtain updated first transfer information and updated second transfer information;

[0019] An extrinsic information log-likelihood ratio is determined based on the updated first transfer information and the updated second transfer information.

[0020] In one embodiment, the step of decoding the external information log-likelihood ratio based on the soft output decoder to obtain soft information includes:

[0021] Based on the soft output decoder, splitting the external information log-likelihood ratio into a first estimated bit sequence and a second estimated bit sequence;

[0022] The first estimated bit sequence and the second estimated bit sequence are decoded according to a preset number of decoding paths to obtain soft information.

[0023] In one embodiment, the step of outputting the bit sequence information after reaching a preset stop condition includes:

[0024] When the generated first estimated bit sequence and the second estimated bit sequence reach a preset stop condition, decoding stops and bit sequence information is output;

[0025] Among them, the preset stop condition includes: the stop criterion of the soft output decoder is met, the elements corresponding to the least reliable bases of the first estimated bit sequence and the second estimated bit sequence have been updated, and the elements corresponding to the most reliable bases of the first estimated bit sequence and the second estimated bit sequence have not been updated.

[0026] In one embodiment, the step of decoding the external information log-likelihood ratio based on the soft output decoder to obtain soft information further includes:

[0027] If the coding length of the symbol sequence data is less than a preset length threshold, decoding the external information log-likelihood ratio based on a soft output decoder algorithm of an ordered likelihood decoder to obtain soft information;

[0028] If the coding length of the symbol sequence data is greater than or equal to a preset length threshold, the external information log-likelihood ratio is decoded based on a belief propagation algorithm to obtain soft information.

[0029] In addition, to achieve the above objectives, the present application also proposes a detection and decoding device based on SCMA, and the detection and decoding device based on SCMA includes:

[0030] A receiving module, used for receiving symbol sequence data corresponding to multiple users;

[0031] A decoding module is used to iteratively decode and detect the symbol sequence data through a preset expected propagation algorithm and a soft output decoder, and output bit sequence information after reaching a preset stop condition, wherein the symbol sequence data includes a plurality of variable nodes and a plurality of function nodes, and the variable nodes and the function nodes are iteratively updated based on prior information.

[0032] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by the processor, the steps of SCMA-based detection and decoding are implemented as described above.

[0033] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, it implements the SCMA-based detection and decoding steps as described above.

[0034] The present application proposes a detection and decoding method, device, storage medium and computer program product based on SCMA. The present application receives symbol sequence data corresponding to multiple users, and then iteratively performs decoding and detection on the symbol sequence data through a preset expected propagation algorithm and a soft output decoder. The variable nodes and function nodes in the symbol sequence data are iteratively updated based on prior information, which can ensure that the bit error rate performance of the detector is improved during the sequence detection process, and then, under the condition of asynchronous transmission, the delay generated during the data transmission process is reduced. Compared with other detection and decoding schemes, the bit error rate is reduced and the reception reliability is enhanced. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0036] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0037] Figure 1 A flowchart of the first embodiment of the detection and decoding method based on SCMA of the present application is provided;

[0038] Figure 2 This is a schematic diagram of the overall execution flow involved in the detection and decoding method based on SCMA in this application;

[0039] Figure 3 A flow chart of the second embodiment of the detection and decoding method based on SCMA of the present application;

[0040] Figure 4 A schematic diagram of the expansion factor of the asynchronous SCMA transmission system involved in Embodiment 2 of the present application is provided;

[0041] Figure 5 A flowchart of the third embodiment of the detection and decoding method based on SCMA of the present application is provided;

[0042] Figure 6 A schematic diagram showing a comparison of BER performances of different detectors involved in the SCMA-based detection and decoding method of this application;

[0043] Figure 7 The first schematic diagram is a comparison of BER performances of different detection and decoding schemes involved in the detection and decoding method based on SCMA in this application;

[0044] Figure 8 This is a schematic diagram comparing the number of BER performance iterations of different detection and decoding schemes involved in the SCMA-based detection and decoding method of this application;

[0045] Fig. 9 A second schematic diagram comparing the number of BER performance iterations of different detection and decoding schemes involved in the SCMA-based detection and decoding method of the present application;

[0046] Fig.10 This is a schematic diagram of the module structure of the detection and decoding device based on SCMA in an embodiment of the present application;

[0047] Fig.11 Schematic diagram of the device structure of the hardware operating environment involved in the SCMA-based detection and decoding method in the embodiment of the present application.

[0048] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0049] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0050] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0051] The main solutions of the embodiments of this application are:

[0052] Receiving symbol sequence data corresponding to multiple users;

[0053] The symbol sequence data is iteratively decoded and detected by a preset expected propagation algorithm and a soft output decoder, and bit sequence information is output after a preset stop condition is reached, wherein the symbol sequence data includes a plurality of variable nodes and a plurality of function nodes, and the variable nodes and the function nodes are iteratively updated based on prior information.

[0054] According to the relevant technology, in satellite-to-ground communications, if strict synchronization is to be achieved, frequent interactions between large-scale satellites and massive terminals cannot be avoided, which will lead to huge signaling overhead and additional power consumption, resulting in high latency and a high actual bit error rate.

[0055] The present application proposes a detection and decoding method, device, storage medium, and computer program product based on SCMA. In the present application, by receiving symbol sequence data corresponding to multiple users, and then through a preset expected propagation algorithm and a soft output decoder, iterative execution of decoding and detection is performed on the symbol sequence data. The variable nodes and function nodes in the symbol sequence data are iteratively updated based on prior information, which can ensure that during the sequence detection process, the bit error rate performance of the detector is improved. Furthermore, under the condition of asynchronous transmission, the delay generated during data transmission is reduced, and the bit error rate is reduced compared to other detection and decoding schemes, enhancing the reception reliability.

[0056] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication, and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device, an SCMA-based detection and decoding device, a receiving end / receiver, etc. that can implement the above functions. Hereinafter, the receiving end is taken as an example to illustrate this embodiment and the following embodiments.

[0057] Based on this, the embodiment of the present application provides a detection and decoding method based on SCMA, referring to Figure 1 , Figure 1 which is a schematic flowchart of the first embodiment of the detection and decoding method based on SCMA of the present application.

[0058] In this embodiment, the detection and decoding method based on SCMA includes steps S10 to S20:

[0059] Step S10, receiving symbol sequence data corresponding to multiple users;

[0060] It should be noted that this embodiment is applied to an asynchronous SCMA-based satellite uplink communication system, which can communicate with multiple different users. The symbol sequence data is the symbol sequence sent to the receiving end after channel coding at the sending end. For example, there are J users sharing K orthogonal resources, usually K < J, which means that each resource is multiplexed by multiple users.

[0061] It should be noted that at the sending end, the encoded bit sequence c j after being interleaved by π j is mapped by an SCMA encoder into a complex-domain SCMA codeword. For the sake of clear symbol representation, the variables before and after interleaving are not distinguished. Assuming that the SCMA codebook size of each user is M, then every Q = log2M encoded bits are mapped into an SCMA codeword x j =(x 1,j , x 2,j , …, x K,j ) T ∈X j, where X j is the codebook set of the j-th user, |X j | = M, and x k,j represents the symbol transmitted by the j-th user on the k-th orthogonal resource.

[0062] SCMA has a sparse property. The K-dimensional SCMA codeword has only d v < K non-zero elements, and their positions are determined by a binary vector f j = (f 1,j , f 2,j , …, f K,j ). T Among them, f k,j = 1 indicates that the j-th user transmits a non-zero symbol x k,j . The entire structure of SCMA can be represented by a K×J-dimensional factor graph matrix F = [f1, f2, …, f J . Let φ k = {j: 1 ≤ j ≤ J, f k,j = 1} and respectively represent the set of user subscripts occupying the k-th orthogonal resource and the set of resource subscripts occupied by the j-th user. For a regular factor graph matrix, there are |φ k | = d f and After SCMA encoding, the total transmitted symbols of the j-th user can be expressed as Among them, is the i-th SCMA codeword transmitted by the j-th user, and F = n′ / log2M is the frame length.

[0063] Let τ j represent the delay of the j-th user, and assume 0 = τ1 < τ2 < … < τ J < T (this application assumes that frame synchronization has been achieved). The signal received from the k-th orthogonal resource can be expressed as:

[0064]

[0065] Among them, represents the channel coefficient of the j-th user on the k-th orthogonal resource in the i-th frame, s(t) is a pulse shaping function with a non-zero interval of [0, T] and unit energy, T is the symbol period, and z k ~CN(0, σ 2 ) represents additive white Gaussian noise. Assume that the delays τ j , can be perfectly estimated at the receiving end.

[0066] It should be noted that after the transmitter sends the symbol sequence data to the receiver, a set of user-oriented matched filters MF (τ j ), To obtain the discrete symbol sequence. For each user j∈φ k , then the discrete symbol obtained by the jth user on the kth orthogonal resource after matched filtering can be expressed as:

[0067]

[0068] in, and is the autocorrelation function, yes Additive white Gaussian noise on . After matched filtering, the discrete symbols Y of all users can be expressed as:

[0069]

[0070] in, and The discrete symbols of all users are the received symbol sequence data.

[0071] Step S20, iteratively decode and detect the symbol sequence data through a preset expected propagation algorithm and a soft output decoder, and output bit sequence information after reaching a preset stop condition, wherein the symbol sequence data includes multiple variable nodes and multiple function nodes, and the variable nodes and the function nodes are iteratively updated based on prior information.

[0072] It should be noted that the preset expectation propagation algorithm can be a parallel expectation propagation algorithm (P-EPA) for multi-user detection under an asynchronous transmission model, and the soft output compiler can be a soft output decoder based on an ordered likelihood decoder (OLD), called soft-output OLD (S-OLD). Compared with existing decoders, S-OLD can generate soft information with high reliability and serve as the input of the detector P-EPA in the embodiment of the present application.

[0073] It should be noted that the preset stop condition may be a preset iterative decoding stop condition. When the decoding result output by the decoder reaches a certain condition, the decoding process is terminated, and the bit sequence information is output at this time.

[0074] It should be noted that the symbol sequence data is represented by an extended factor graph, which includes multiple sub-factor graphs. Assuming there are F sub-factor graphs, the corresponding data is transmitted F times, and each sub-factor graph contains J prior nodes, J variable nodes (VNs) and Jd v Function Nodes (FNs), VNs and FNs represent the transmitted codewords and received discrete symbols respectively, the prior nodes represent the prior probabilities of the codewords, the edges within the sub-factor graph represent the information transfer between VNs and FNs, and the edges connecting the sub-factor graphs represent the information transfer between the sub-factor graphs.

[0075] It should be noted that in the process of iterative updating, reasonable initial value settings have a greater impact on the detection performance. In traditional initialization methods, variable nodes and function nodes are usually assigned constant values. Under this initialization method, both the detection and decoding methods do not take prior information into account. Therefore, the performance of the detector may be poor, or the bit error rate of the decoded data may be high. In this embodiment, the variable nodes and function nodes are initialized based on the prior information and then iteratively updated, thereby ensuring the performance of the detector.

[0076] Specifically, the overall execution flow diagram involved in this embodiment is as follows: Figure 2 As shown by Figure 2 It can be obtained that at the transmitting end, each user bit information sequence u j First, channel coding is performed, and then j After interleaving, SCMA encoding is performed, and then each SCMA symbol is subjected to a pulse shaping function to obtain a transmit symbol.

[0077] At the receiving end, the transmitted symbol sequence is received on each orthogonal resource, and after filtering by the matched filter for each user, the symbol sequence Y is obtained. When performing detection and decoding for each user according to Y, the detector is executed first, that is, the proposed P-EPA algorithm.

[0078] P-EPA outputs soft information after the test is completed After deinterleaving, it is sent to the soft output decoder, and the decoder outputs the soft information after decoding. After interleaving, it is sent to the detector. The decoding and detection parts are performed iteratively. When the results of all users are the same twice in a row, the iteration process is stopped and the estimated information bit sequence is output.

[0079] In a feasible implementation manner, the step S20 of iteratively decoding and detecting the symbol sequence data by using a preset expected propagation algorithm and a soft output decoder, and outputting the bit sequence information after reaching a preset stop condition, comprises:

[0080] By using a preset expectation propagation algorithm, the symbol sequence data is iteratively updated to obtain an external information log-likelihood ratio;

[0081] It should be noted that the preset expected propagation algorithm is a detection algorithm in the P-EPA detector, and the steps of iteratively updating the symbol sequence data may be:

[0082] The function nodes and variable nodes are initialized by prior probabilities, and then the transmission codewords and received discrete symbols corresponding to the two nodes are iteratively updated respectively. Finally, before the updated data are input into the decoder, the external information log-likelihood ratio needs to be generated based on these updated data.

[0083] It should be noted that the extrinsic information log-likelihood ratio (ELR) is a key component of the iterative decoding algorithm, which provides a more accurate probability estimate by comprehensively considering the information of other bits. With the help of extrinsic information LLR, the decoder can not only effectively correct random errors, but also adapt to different channel conditions and coding schemes to ensure efficient and reliable data transmission.

[0084] Inputting the external information log-likelihood ratio into a soft output decoder, and decoding the external information log-likelihood ratio based on the soft output decoder to obtain soft information;

[0085] It should be noted that the soft output decoder not only provides hard decision results (ie, the estimated value of each bit 0 or 1), but also outputs a reliability metric (such as LLR) for each bit for subsequent processing or use in combination with other decoding algorithms.

[0086] It should be noted that the decoding process performed by the soft output decoder may be:

[0087] 1. Receive extrinsic information LLR: Receive each bit of extrinsic information LLR from multi-user detection (MUD) or other pre-processing modules.

[0088] 2. Initialization: Initialize the variable node message according to the received external information LLR.

[0089] 3. Iterative decoding:

[0090] 1) Message passing: Execute the belief propagation (BP) algorithm on the factor graph to update the probability estimate of each bit by passing messages between the check nodes and the variable nodes.

[0091] 2) External information update: In each iteration, the external information LLR of each variable node is calculated and updated, considering the new messages from all connected check nodes.

[0092] 4. Final decision:

[0093] Hard decision: Determine the estimated value of each bit based on the final variable node message.

[0094] Soft output: Outputs the LLR of each bit as soft information for subsequent processing or use in conjunction with other decoding algorithms.

[0095] The soft information is iteratively exchanged between a detector corresponding to a preset expected propagation algorithm and a soft output decoder until a preset stop condition is reached, and then bit sequence information is output.

[0096] It should be noted that the obtained soft information is iteratively exchanged between the detector and the soft output decoder. When the number of iterations reaches the maximum preset number or the decoding results of all users converge, the detection and decoding process is terminated. At this point, the final estimated information sequence, that is, the bit sequence information, is output.

[0097] In a feasible implementation manner, the step of decoding the external information log-likelihood ratio based on the soft output decoder to obtain soft information further includes:

[0098] If the coding length of the symbol sequence data is less than a preset length threshold, decoding the external information log-likelihood ratio based on a soft output decoder algorithm of an ordered likelihood decoder to obtain soft information;

[0099] If the coding length of the symbol sequence data is greater than or equal to a preset length threshold, the external information log-likelihood ratio is decoded based on a belief propagation algorithm to obtain soft information.

[0100] It should be noted that the preset length threshold may be a preset code length value, which is used to measure whether the length of the channel coding is a long code or a short code. If the channel coding adopts a short LDPC code, the S-OLD algorithm corresponding to the above-mentioned soft output decoder may be used. If a long LDPC code is used, the belief propagation algorithm may be used. The decoder outputs the soft information after decoding. After interleaving, it is sent to the detector. The decoding and detection parts are iteratively performed to obtain soft information.

[0101] It should be noted that the channel coding part includes but is not limited to LDPC code, and any coding scheme that can output soft information can be applied to the embodiments of the present application.

[0102] The present application proposes a detection and decoding method, device, storage medium and computer program product based on SCMA. The present application receives symbol sequence data corresponding to multiple users, and then iteratively performs decoding and detection on the symbol sequence data through a preset expected propagation algorithm and a soft output decoder. The variable nodes and function nodes in the symbol sequence data are iteratively updated based on prior information, which can ensure that the bit error rate performance of the detector is improved during the sequence detection process, and then, under the condition of asynchronous transmission, the delay generated during the data transmission process is reduced. Compared with other detection and decoding schemes, the bit error rate is reduced and the reception reliability is enhanced.

[0103] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can be referred to the above introduction, and will not be repeated in the following. Figure 3 , before the step of iteratively updating the symbol sequence data by using a preset expectation propagation algorithm to obtain the external information log-likelihood ratio, it also includes:

[0104] Step S100, constructing an expansion factor graph based on the asynchronous interference relationship between the symbol sequence data;

[0105] The extended factor graph includes a plurality of sub-factor graphs corresponding to the number of transmissions, each of the sub-factor graphs includes a plurality of prior nodes, variable nodes and function nodes, the prior nodes of the extended factor graph represent the prior probability of the codeword, the variable nodes represent the transmitted codeword, and the function nodes represent the received discrete symbols.

[0106] It should be noted that due to symbol asynchrony, The interference symbol is interfered by the symbols transmitted by other users in two consecutive symbol periods. and According to the above interference relationship, a corresponding extended factor graph is constructed to characterize the interference in the whole frame.

[0107] It should be noted that if Figure 4 As shown in Figure 1, the extended factor graph consists of F sub-factor graphs, which correspond to F transmissions. Each sub-factor graph contains J prior nodes, J variable nodes (VNs) and Jd vFunction Nodes (FNs). VNs and FNs represent the transmitted codewords and received discrete symbols, respectively. Prior nodes represent the prior probabilities of codewords. The edges within the sub-factor graph represent the information transfer between VNs and FNs, and the edges connecting the sub-factor graphs represent the information transfer between the sub-factor graphs.

[0108] In a feasible implementation manner, the step of iteratively updating the symbol sequence data by using a preset expectation propagation algorithm to obtain the external information log-likelihood ratio includes:

[0109] Based on the prior nodes, the variable nodes and the function nodes, determining the posterior probability corresponding to the symbol sequence data, the first transfer information transmitted from the variable node to the function node and the second transfer information transmitted from the function node to the variable node;

[0110] It should be noted that for the i-th transmission, let and Indicates that in the tth iteration The posterior probability, from VN Pass to FN Information transmitted in the opposite direction. Pass to FN The information is the first transmission information transmitted from the variable node to the function node, and the second transmission information transmitted from the function node to the variable node is the information transmitted from the FN Pass to VN information.

[0111] Converting the first transfer information and / or the second transfer information into Gaussian distribution information to obtain first distribution information and second distribution information;

[0112] It should be noted that Represents the prior node If the decoder does not transmit any information, the prior information of all transmitted codewords is the same, that is, Expectation propagation transforms complex posterior distributions into simple distributions through distribution projection.

[0113] Specifically, expectation propagation approximates the message passing between VNs and FNs as a Gaussian distribution, so It can be described by its mean and variance. The Gaussian distribution is expressed as:

[0114]

[0115]

[0116] Among them and Respectively refer to the first distribution information and the second distribution information.

[0117] Iteratively calculating the first distribution information and the second distribution information respectively to obtain updated first transfer information and updated second transfer information;

[0118] It should be noted that the steps of iteratively calculating the first distribution information and the second distribution information may be:

[0119] 1. Initialize the first distribution information and the second distribution information;

[0120] Reasonable initial value and The detection performance is greatly affected. In traditional initialization methods, they are usually assigned constant values. However, these methods do not take into account prior information. This application proposes a new initialization method to improve the performance of the detector. The key is and The initialization of is calculated using the prior probability, which can be expressed as follows:

[0121]

[0122] After initialization, the information update of FNs and VNs is performed iteratively.

[0123] 2. FNs update;

[0124] Discrete symbols By user m, m∈φ k \jTransmitted symbols and noise interference. Assume and From the previous iteration, we have obtained and It can be calculated as:

[0125]

[0126] 3. VNs update.

[0127] Posterior probability It can be calculated as:

[0128]

[0129] Among them, a j ∈X j , It can be calculated as:

[0130]

[0131] Then, the posterior mean and variance can be calculated as:

[0132]

[0133]

[0134] For simplicity, the information update of VNs is expressed as:

[0135]

[0136] It can be seen that the proposed P-EPA can be executed in parallel.

[0137] It should be noted that the updated first transmission information may be an updated transmission codeword, and the updated second transmission information may be an updated discrete symbol.

[0138] An extrinsic information log-likelihood ratio is determined based on the updated first transfer information and the updated second transfer information.

[0139] It should be noted that determining the log-likelihood ratio of the external information based on the updated first transfer information (prior information LLR) and the updated second transfer information (variable node message) is an important means to improve decoding performance. By accurately calculating and iteratively updating the external information LLR, the decoder can more accurately estimate the true value of each bit, thereby reducing the bit error rate and enhancing the robustness of the system.

[0140] In this embodiment, the variable node information and the function node information are initialized and iteratively updated based on the prior probability, so as to improve the performance of the detector.

[0141] Based on the first embodiment and / or the second embodiment of the present application, in the third embodiment of the present application, the same or similar contents as those of the above-mentioned first and second embodiments can refer to the above introduction, and will not be repeated later. Figure 5 The step of decoding the external information log-likelihood ratio based on the soft output decoder to obtain soft information comprises:

[0142] Step S110, based on the soft output decoder, splitting the external information log likelihood ratio into a first estimated bit sequence and a second estimated bit sequence;

[0143] It should be noted that represents the a posteriori LLRs obtained by the jth user from the decoder. According to the Max-Log-MAP criterion, it can be calculated as follows:

[0144]

[0145] make yes In S-OLD, the external information LLR obtained by the n-th coded bit from the decoder can be expressed as:

[0146]

[0147] in, It is c j (n:b) and The weighted Hamming distance (WHD) of j (n:b) is and There is an estimated coded bit sequence with minimum WHD and its nth bit is b, b = 0, 1. However, it is directly obtained Too complicated. Here is a low-complexity method to get Approximate results of .

[0148] It should be noted that, in the decoder, in order to find the optimal estimated coded bit sequence, a certain number of TEPs are generated in an approximately increasing order of TEP likelihood.

[0149] After recoding a TEP, an estimated coded bit sequence is obtained, and thus a certain number of estimated coded bit sequences and their WHDs are obtained. and Respectively represent the decoding process and Then, two vectors are established to first estimate the bit sequence and the second estimated bit sequence Initialized as,

[0150] Step S120: decoding the first estimated bit sequence and the second estimated bit sequence according to a preset number of decoding paths to obtain soft information.

[0151] It should be noted that in the OLD (Optimized Likelihood Decoding) process, in order to find the optimal estimated coded bit sequence, a certain number of trial error paths (TEPs) are indeed generated according to a certain strategy. These TEPs are generated in an approximate increasing order of their likelihood, with the goal of improving decoding performance by gradually approaching the most likely transmitted codeword. This method combines soft decision information and iterative search techniques to achieve efficient and accurate decoding.

[0152] In a feasible implementation manner, the step of outputting the bit sequence information after reaching a preset stop condition includes:

[0153] When the generated first estimated bit sequence and the second estimated bit sequence reach a preset stop condition, decoding stops and bit sequence information is output;

[0154] Among them, the preset stop condition includes: the stop criterion of the soft output decoder is met, the elements corresponding to the least reliable bases of the first estimated bit sequence and the second estimated bit sequence have been updated, and the elements corresponding to the most reliable bases of the first estimated bit sequence and the second estimated bit sequence have not been updated.

[0155] It should be noted that in the OLD decoding process, when an estimated coded bit sequence c is generated j After that, the two vectors are updated as like Decoding terminates when the following conditions are met: 1) the stop criterion of OLD is met, 2) D (0) and D (1) The elements corresponding to the least reliable basis in have been updated. (0) and D (1) The elements corresponding to the most reliable basis in are not updated, so we need to perform 1st-order statistical decoding on these positions and update them. Then, the extrinsic LLRs of the jth user are It can be calculated according to the above formula.

[0156] In iterative detection and decoding, the external information LLR The prior probability of transforming into SCMA codeword can be calculated as follows:

[0157]

[0158] P(c n,j =0) = 1-P(c n,j =1)

[0159]

[0160] in, express The qth bit of the mapped bit sequence.

[0161] It should be noted that, in combination with the above P-EPA and S-OLD, the proposed detection and decoding scheme iteratively exchanges information between the detector and the decoder. Let To and Ts denote the maximum number of iterations of the outer iteration and the SCMA detector, respectively. When To is reached or the decoding results of all users converge, the proposed AIDD (asynchronous assisted iterative detection and decoding scheme) will terminate. When the outer iteration reaches the Toth time, the decoding result of the jth user It is expressed as:

[0162]

[0163] The proposed AIDD scheme The corresponding estimated information sequence Can be obtained from get.

[0164] In this embodiment, the external information likelihood ratio is decoded by a soft output decoder, which generates soft information with high reliability compared with the existing decoder, thereby improving the error performance of the overall system.

[0165] In order to prove the effectiveness of the proposed initialization method, the average bit error rate (BER) performance of the proposed P-EPA is first analyzed. For comparison, the synchronous MPA and the asynchronous original EPA are also simulated, and the maximum number of iterations for all schemes is 6. In addition, F=64 is set, and two different codebooks with M=4 are used in the simulation, for example, the Huawei codebook and the Rayleigh codebook. Figure 6 As shown in the figure, the performance of asynchronous SCMA is better than that of synchronous SCMA under low signal-to-noise ratio. The performance of the P-EPA proposed in this application is better than that of the original EPA on the Huawei codebook. For the Rayleigh codebook, the proposed P-EPA is also better than the original EPA, and there is no error flattening. In addition, under the same simulation conditions as above, MATLAB was used to execute on a 3.0GHz CPU, and the average detection time per frame of the proposed P-EPA and the original EPA was calculated. As shown in Table 1, the detection time of the proposed P-EPA is much shorter than that of the original EPA.

[0166] Table 1 Average detection time per frame for asynchronous SCMA

[0167]

[0168] like Figure 7As shown, the simulation uses LDPC code C(128,64) and Rayleigh codebook. The comparison schemes include independent detection and decoding scheme (individual) of asynchronous SCMA, synchronous IDD (iterative detection and decoding, IDD), synchronous JDD (JDD / MPA) based on MPA (message passing algorithm) (joint detection and decoding), synchronous JDD based on approximate EPA (JDD / EPA) and single-user scheme. Note that the single-user scheme is implemented by AIDD, which assumes that the receiver is fully aware of the transmitted signals of other users. Therefore, the single-user performance is the limit of the AIDD scheme. We set To=2,3, Ts=3 and BP 3 iterations. For fairness, the maximum number of iterations of the individual scheme is set to 9, and the maximum number of iterations of JDD / MPA and JDD / EPA are both set to T=6,9.

[0169] Compared with the individual scheme, IDD, JDD / MPA and JDD / EPA, the proposed AIDD has the best performance, and can obtain about 1.6dB, 3.4dB, 3.6dB and 6.2dB gains respectively when the average BER = 10-4. In addition, it can be seen that the proposed AIDD scheme can approach the performance of the single-user scheme. In addition, as Figure 8 As shown in the figure, the average number of outer iterations of the synchronous IDD and the proposed AIDD schemes, as well as the equivalent average number of iterations of the two JDD schemes, are also analyzed. It can be seen from the figure that under low SNR conditions, the average number of iterations of the AIDD algorithm proposed in this application is much less than that of other schemes, and converges to 2 times as the SNR increases.

[0170] Finally, the decoder in the asynchronous-assisted iterative detection and decoding (AIDD) scheme is replaced with the belief propagation (BP) decoding algorithm of LDPC code, and the performance of the proposed AIDD scheme under long LDPC code is evaluated. C(1056,792) The comparison schemes include the independent detection and decoding scheme of asynchronous SCMA (individual), synchronous IDD, and synchronous JDD based on MPA (JDD / MPA). We set To = 2, 3, Ts = 4, and BP 4 iterations. For fairness, the maximum number of iterations of the individual scheme is set to 12, and the maximum number of iterations of JDD / MPA and JDD / EPA are both set to T = 8, 12. Fig. 9As shown, compared with JDD / MPA, IDD, and individual schemes, the proposed AIDD has the best performance, and can obtain about 0.65dB, 0.75dB and 1.30dB gains respectively when the average BER = 10-4.

[0171] In summary, the embodiment of the present application proposes an ultra-reliable receiver for SCMA in satellite-to-earth communications, which iteratively exchanges information between the detector and the decoder while taking into account the delay. First, we proposed a parallel expectation propagation algorithm (P-EPA) based on an expansion factor graph, and gave a new initialization method using prior information to improve the detection performance. Then, a soft output decoder called S-OLD is proposed to effectively generate soft information and serve as the input of the P-EPA proposed in AIDD. Finally, a stopping criterion is designed to terminate detection and decoding when the maximum number of iterations is reached or the decoding results of all users converge. Compared with similar schemes and synchronous schemes, the proposed P-EPA and AIDD schemes have the advantages of short detection time and good error performance, which are of great significance to the design of SCMA receivers in satellite-to-earth communications.

[0172] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the detection and decoding based on SCMA of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0173] This application also provides a detection and decoding device based on SCMA, please refer to Fig.10 , the SCMA-based detection and decoding device comprises:

[0174] A receiving module 10, configured to receive symbol sequence data corresponding to multiple users;

[0175] The decoding module 20 is used to iteratively decode and detect the symbol sequence data through a preset expected propagation algorithm and a soft output decoder, and output bit sequence information after reaching a preset stop condition, wherein the symbol sequence data includes multiple variable nodes and multiple function nodes, and the variable nodes and the function nodes are iteratively updated based on prior information.

[0176] Optionally, the decoding module includes:

[0177] A processing unit, configured to perform iterative updating processing on the symbol sequence data by using a preset expectation propagation algorithm to obtain an external information log-likelihood ratio;

[0178] A decoding processing unit, configured to input the external information log-likelihood ratio into a soft output decoder, and perform decoding processing on the external information log-likelihood ratio based on the soft output decoder to obtain soft information;

[0179] The exchange processing unit is used to iteratively exchange the soft information between the detector corresponding to the preset expected propagation algorithm and the soft output decoder until a preset stop condition is reached, and then output the bit sequence information.

[0180] Optionally, the decoding module further includes:

[0181] A construction unit, configured to construct an expansion factor graph based on the asynchronous interference relationship between the symbol sequence data;

[0182] The extended factor graph includes a plurality of sub-factor graphs corresponding to the number of transmissions, each of the sub-factor graphs includes a plurality of prior nodes, variable nodes and function nodes, the prior nodes of the extended factor graph represent the prior probability of the codeword, the variable nodes represent the transmitted codeword, and the function nodes represent the received discrete symbols.

[0183] Optionally, the processing unit includes:

[0184] A first determining subunit, configured to determine, based on the prior nodes, the variable nodes and the function nodes, the posterior probability corresponding to the symbol sequence data, the first transfer information transmitted from the variable node to the function node, and the second transfer information transmitted from the function node to the variable node;

[0185] A conversion subunit, configured to convert the first transfer information and / or the second transfer information into Gaussian distribution information to obtain first distribution information and second distribution information;

[0186] an iterative calculation subunit, configured to perform iterative calculations on the first distribution information and the second distribution information respectively, to obtain updated first transfer information and updated second transfer information;

[0187] The second determining subunit is used to determine the log-likelihood ratio of the extrinsic information based on the updated first transfer information and the updated second transfer information.

[0188] Optionally, the decoding processing unit includes:

[0189] A splitting subunit, configured to split the extrinsic information log-likelihood ratio into a first estimated bit sequence and a second estimated bit sequence based on the soft output decoder;

[0190] The decoding processing subunit is used to perform decoding processing on the first estimated bit sequence and the second estimated bit sequence according to a preset number of decoding paths to obtain soft information.

[0191] Optionally, the decoding module includes:

[0192] an output unit, configured to stop decoding and output bit sequence information when the generated first estimated bit sequence and the second estimated bit sequence reach a preset stop condition;

[0193] Among them, the preset stop condition includes: the stop criterion of the soft output decoder is met, the elements corresponding to the least reliable bases of the first estimated bit sequence and the second estimated bit sequence have been updated, and the elements corresponding to the most reliable bases of the first estimated bit sequence and the second estimated bit sequence have not been updated.

[0194] Optionally, the decoding processing unit further includes:

[0195] A first processing subunit is configured to decode the external information log-likelihood ratio based on a soft output decoder algorithm of an ordered likelihood decoder to obtain soft information if the encoding length of the symbol sequence data is less than a preset length threshold;

[0196] The second processing subunit is used to decode the external information log-likelihood ratio based on a belief propagation algorithm to obtain soft information if the coding length of the symbol sequence data is greater than or equal to a preset length threshold.

[0197] The SCMA-based detection and decoding device provided in the present application adopts the SCMA-based detection and decoding in the above-mentioned embodiments, which can solve the technical problems of SCMA-based detection and decoding. Compared with the prior art, the beneficial effects of the SCMA-based detection and decoding device provided in the present application are the same as the beneficial effects of the SCMA-based detection and decoding provided in the above-mentioned embodiments, and the other technical features in the SCMA-based detection and decoding device are the same as the features disclosed in the above-mentioned embodiment method, which will not be repeated here.

[0198] The present application provides a detection and decoding device based on SCMA, and the detection and decoding device based on SCMA includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can perform the SCMA-based detection and decoding in the above-mentioned embodiment one.

[0199] Reference below Fig.11, which shows a schematic diagram of the structure of a detection and decoding device based on SCMA suitable for implementing an embodiment of the present application. The detection and decoding device based on SCMA in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Fig.11 The SCMA-based detection and decoding device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0200] like Fig.11 As shown, the SCMA-based detection and decoding device may include a processing device 1001 (such as a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the SCMA-based detection and decoding device are also stored. The processing device 1001, ROM1002, and RAM1004 are connected to each other via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 1003 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 1009. The communication device 1009 can allow the SCMA-based detection and decoding device to communicate wirelessly or wired with other devices to exchange data. Although the SCMA-based detection and decoding device with various systems is shown in the figure, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems may be implemented or provided alternatively.

[0201] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0202] The SCMA-based detection and decoding device provided in the present application adopts the SCMA-based detection and decoding in the above-mentioned embodiment, which can solve the technical problems of SCMA-based detection and decoding. Compared with the prior art, the beneficial effects of the SCMA-based detection and decoding device provided in the present application are the same as the beneficial effects of the SCMA-based detection and decoding provided in the above-mentioned embodiment, and the other technical features in the SCMA-based detection and decoding device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0203] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0204] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0205] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the SCMA-based detection and decoding method in the above-mentioned embodiment.

[0206] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0207] The computer-readable storage medium may be included in the SCMA-based detection and decoding device; or may exist independently without being assembled into the SCMA-based detection and decoding device.

[0208] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the SCMA-based detection and decoding device, the SCMA-based detection and decoding device:

[0209] Receiving symbol sequence data corresponding to multiple users;

[0210] The symbol sequence data is iteratively decoded and detected by a preset expected propagation algorithm and a soft output decoder, and bit sequence information is output after a preset stop condition is reached, wherein the symbol sequence data includes a plurality of variable nodes and a plurality of function nodes, and the variable nodes and the function nodes are iteratively updated based on prior information.

[0211] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0212] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0213] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0214] The readable storage medium provided in this application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for performing the above-mentioned detection and decoding based on SCMA, and can solve the technical problems of detection and decoding based on SCMA. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as the beneficial effects of detection and decoding based on SCMA provided in the above embodiments, and will not be repeated here.

[0215] The present application also provides a computer program product, including a computer program, which implements the above-mentioned SCMA-based detection and decoding steps when executed by a processor.

[0216] The computer program product provided in this application can solve the technical problems of detection and decoding based on SCMA. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as the beneficial effects of detection and decoding based on SCMA provided in the above embodiments, which will not be repeated here.

[0217] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A detection and decoding method based on SCMA, characterized in that: Applied to the receiving end, the method includes: Receiving symbol sequence data corresponding to multiple users; The symbol sequence data is iteratively decoded and detected by a preset expected propagation algorithm and a soft output decoder, and bit sequence information is output after a preset stop condition is reached, wherein the symbol sequence data includes a plurality of variable nodes and a plurality of function nodes, and the variable nodes and the function nodes are iteratively updated based on prior information.

2. The method according to claim 1, characterized in that The step of iteratively decoding and detecting the symbol sequence data by using a preset expected propagation algorithm and a soft output decoder, and outputting the bit sequence information after reaching a preset stop condition, comprises: By using a preset expectation propagation algorithm, the symbol sequence data is iteratively updated to obtain an external information log-likelihood ratio; Inputting the external information log-likelihood ratio into a soft output decoder, and decoding the external information log-likelihood ratio based on the soft output decoder to obtain soft information; The soft information is iteratively exchanged between a detector corresponding to a preset expected propagation algorithm and a soft output decoder until a preset stop condition is reached, and then bit sequence information is output.

3. The method according to claim 2, characterized in that Before the step of iteratively updating the symbol sequence data by using a preset expectation propagation algorithm to obtain the external information log-likelihood ratio, the method further includes: Constructing an expansion factor graph based on the asynchronous interference relationship between the symbol sequence data; The extended factor graph includes a plurality of sub-factor graphs corresponding to the number of transmissions, each of the sub-factor graphs includes a plurality of prior nodes, variable nodes and function nodes, the prior nodes of the extended factor graph represent the prior probability of the codeword, the variable nodes represent the transmitted codeword, and the function nodes represent the received discrete symbols.

4. The method according to claim 3, characterized in that The step of iteratively updating the symbol sequence data by using a preset expectation propagation algorithm to obtain the external information log-likelihood ratio comprises: Based on the prior nodes, the variable nodes and the function nodes, determining the posterior probability corresponding to the symbol sequence data, the first transfer information transmitted from the variable node to the function node and the second transfer information transmitted from the function node to the variable node; Converting the first transfer information and / or the second transfer information into Gaussian distribution information to obtain first distribution information and second distribution information; Iteratively calculating the first distribution information and the second distribution information respectively to obtain updated first transfer information and updated second transfer information; An extrinsic information log-likelihood ratio is determined based on the updated first transfer information and the updated second transfer information.

5. The method according to claim 2, characterized in that The step of decoding the external information log-likelihood ratio based on the soft output decoder to obtain soft information comprises: Based on the soft output decoder, splitting the external information log-likelihood ratio into a first estimated bit sequence and a second estimated bit sequence; The first estimated bit sequence and the second estimated bit sequence are decoded according to a preset number of decoding paths to obtain soft information.

6. The method according to claim 5, characterized in that The step of outputting the bit sequence information after reaching a preset stop condition comprises: When the generated first estimated bit sequence and the second estimated bit sequence reach a preset stop condition, decoding stops and bit sequence information is output; Among them, the preset stop condition includes: the stop criterion of the soft output decoder is met, the elements corresponding to the least reliable bases of the first estimated bit sequence and the second estimated bit sequence have been updated, and the elements corresponding to the most reliable bases of the first estimated bit sequence and the second estimated bit sequence have not been updated.

7. The method according to claim 2, characterized in that The step of decoding the external information log-likelihood ratio based on the soft output decoder to obtain soft information further includes: If the coding length of the symbol sequence data is less than a preset length threshold, decoding the external information log-likelihood ratio based on a soft output decoder algorithm of an ordered likelihood decoder to obtain soft information; If the coding length of the symbol sequence data is greater than or equal to a preset length threshold, the external information log-likelihood ratio is decoded based on a belief propagation algorithm to obtain soft information.

8. A detection and decoding device based on SCMA, characterized in that: The device comprises: A receiving module, used for receiving symbol sequence data corresponding to multiple users; A decoding module is used to iteratively decode and detect the symbol sequence data through a preset expected propagation algorithm and a soft output decoder, and output bit sequence information after reaching a preset stop condition, wherein the symbol sequence data includes a plurality of variable nodes and a plurality of function nodes, and the variable nodes and the function nodes are iteratively updated based on prior information.

9. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of SCMA-based detection and decoding are implemented as described in any one of claims 1 to 7.

10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of SCMA-based detection and decoding are implemented as claimed in any one of claims 1 to 7.

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