Detection and decoding methods based on scma and related devices

Abstract

This application discloses a detection and decoding method and related equipment based on SCMA, relating to the field of communication technology. The SCMA-based detection and decoding method includes: receiving symbol sequence data corresponding to multiple users; iteratively decoding and detecting the symbol sequence data using a preset expectation propagation algorithm and a soft-output decoder; and outputting bit sequence information after reaching a preset stopping condition. The symbol sequence data includes multiple variable nodes and multiple functional nodes, which are iteratively updated based on prior information. This application reduces the bit error rate during data transmission.

Description

Technical Field

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

[0002] In satellite-to-ground communication, Sparse Code Multiple Access (SCMA) is one of the promising multiple access candidate technologies. One proposed technology is a joint detection and decoding (JDD) scheme, which combines SCMA detection and low-density parity-check (LDPC) decoding under a single joint sparse graph. This approach is based on the assumption of complete synchronization of multi-user signals at the receiver. However, in satellite-to-ground communication, achieving strict synchronization inevitably involves frequent interactions between a large number of satellites and numerous terminals. This leads to significant signaling overhead and additional power consumption, resulting in high latency and a relatively high actual bit error rate.

[0003] The above content is only used to help understand the technical solution of this application and does not represent an admission that the above content is prior art. Summary of the Invention

[0004] The main objective 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 latency and high actual bit error rate in satellite-to-ground communication.

[0005] To achieve the above objectives, this application proposes an SCMA-based detection and decoding method, which includes:

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

[0007] The symbol sequence data is iteratively decoded and detected using a preset expectation propagation algorithm and a soft output decoder. After reaching a preset stopping condition, bit sequence information is output. The symbol sequence data includes multiple variable nodes and multiple functional nodes, which are iteratively updated based on prior information.

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

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

[0010] The external information log-likelihood ratio is input to a soft-output decoder. Based on the soft-output decoder, the external information log-likelihood ratio is decoded to obtain soft information.

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

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

[0013] Based on the asynchronous interference relationship between the symbol sequence data, an expansion factor graph is constructed;

[0014] The extended factor graph includes multiple sub-factor graphs corresponding to the number of transmissions. Each sub-factor graph includes multiple prior nodes, variable nodes, and functional 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 functional nodes represent the received discrete symbols.

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

[0016] Based on the prior node, variable node, and functional node, determine the posterior probability corresponding to the symbol sequence data, the first transmission information from the variable node to the functional node, and the second transmission information from the functional node to the variable node.

[0017] Based on the first and / or second transmission information, the information is converted into Gaussian distribution information to obtain first distribution information and second distribution information;

[0018] The first distribution information and the second distribution information are iteratively calculated to obtain the updated first transmission information and the updated second transmission information.

[0019] Based on the updated first and second transmission information, the external information log-likelihood ratio is determined.

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

[0021] Based on the soft-output decoder, the external information log-likelihood ratio is split 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 bit sequence information after reaching a preset stopping condition includes:

[0024] Decoding stops when the generated first estimated bit sequence and second estimated bit sequence reach the preset stopping condition, and bit sequence information is output.

[0025] The preset stopping conditions include: satisfying the stopping criteria of the soft output decoder, the elements corresponding to the least reliable basis of both the first estimated bit sequence and the second estimated bit sequence have been updated, and the elements corresponding to the most reliable basis of both the first estimated bit sequence and the second estimated bit sequence have not been updated.

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

[0027] If the encoding length of the symbol sequence data is less than a preset length threshold, then the soft output decoder algorithm based on the ordered likelihood decoder decodes the log-likelihood ratio of the extrinsic information to obtain soft information.

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

[0029] Furthermore, to achieve the above objectives, this application also proposes an SCMA-based detection and decoding apparatus, which includes:

[0030] The receiving module is used to receive symbol sequence data corresponding to multiple users;

[0031] The decoding module is used to iteratively decode and detect the symbol sequence data using a preset expectation propagation algorithm and a soft output decoder. After reaching a preset stopping condition, it outputs bit sequence information. The symbol sequence data includes multiple variable nodes and multiple functional nodes, which are iteratively updated based on prior information.

[0032] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the SCMA-based detection and decoding steps described above.

[0033] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the SCMA-based detection and decoding steps described above.

[0034] This application proposes a detection and decoding method, apparatus, storage medium, and computer program product based on SCMA. This application receives symbol sequence data corresponding to multiple users, and then iteratively decodes and detects the symbol sequence data using a preset expectation propagation algorithm and a soft-output decoder. The variable nodes and functional nodes in the symbol sequence data are iteratively updated based on prior information, which can ensure improved bit error rate performance of the detector during sequence detection. Furthermore, under asynchronous transmission conditions, it reduces the latency generated during data transmission, lowering the bit error rate and enhancing reception reliability compared to other detection and decoding schemes. Attached Figure Description

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

[0036] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1 This is a flowchart illustrating an embodiment of the SCMA-based detection and decoding method of this application.

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

[0039] Figure 3 This is a flowchart illustrating Embodiment 2 of the SCMA-based detection and decoding method of this application;

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

[0041] Figure 5 This is a flowchart illustrating Embodiment 3 of the SCMA-based detection and decoding method of this application;

[0042] Figure 6 This is a schematic diagram comparing the BER performance of different detectors involved in the SCMA-based detection and decoding method of this application;

[0043] Figure 7 The first schematic diagram shows a comparison of the BER performance of different detection and decoding schemes involved in the SCMA-based detection and decoding method of this application.

[0044] Figure 8 This diagram illustrates the comparison of BER performance iteration counts for different detection and decoding schemes involved in the SCMA-based detection and decoding method of this application.

[0045] Figure 9 The second schematic diagram shows a comparison of the BER performance iterations of different detection and decoding schemes involved in the SCMA-based detection and decoding method of this application.

[0046] Figure 10 This is a schematic diagram of the module structure of the SCMA-based detection and decoding device according to an embodiment of this application;

[0047] Figure 11 This is a schematic diagram of the device structure of the hardware operating environment involved in the SCMA-based detection and decoding method in the embodiments of this application.

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

[0049] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0050] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0051] The main solution in this application's embodiments is:

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

[0053] The symbol sequence data is iteratively decoded and detected using a preset expectation propagation algorithm and a soft output decoder. After reaching a preset stopping condition, bit sequence information is output. The symbol sequence data includes multiple variable nodes and multiple functional nodes, which are iteratively updated based on prior information.

[0054] In satellite-to-ground communication, if strict synchronization is to be achieved, frequent interactions between a large number of satellites and a massive number of terminals cannot be avoided. This results in huge signaling overhead and additional power consumption, leading to high latency and a relatively 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, symbol sequence data corresponding to multiple users is received, and then the symbol sequence data is iteratively decoded and detected 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. Furthermore, under the condition of asynchronous transmission, the delay generated during the data transmission process is reduced, and the bit error rate is reduced compared with 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 capable of implementing the above functions, a detection and decoding device based on SCMA, a receiving end / receiver, etc. Hereinafter, the receiving end will be taken as an example to illustrate this embodiment and the following embodiments.

[0057] Based on this, an 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 coded bit sequence c j after being interleaved by π j is mapped by the 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 coded bits are mapped into a 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<o 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[[ID=​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​It should be noted that after the sending end transmits symbol sequence data to the receiving end, a set of user-oriented matched filters MF(τ) is deployed at the receiving end. j ), The resulting discrete symbol sequence. For each user j∈φ k Then, the discrete symbol obtained by the j-th user after matched filtering on the k-th orthogonal resource can be represented as:

[0067]

[0068] in, and It is an autocorrelation function. yes Additive white Gaussian noise. After matched filtering, the discrete symbolic Y for all users can be represented as:

[0069]

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

[0071] Step S20: The symbol sequence data is iteratively decoded and detected using a preset expectation propagation algorithm and a soft output decoder. After reaching a preset stopping condition, bit sequence information is output. The symbol sequence data includes multiple variable nodes and multiple functional nodes, which are iteratively updated based on prior information.

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

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

[0074] It should be noted that symbolic sequence data is represented by an expanded factor graph, which includes multiple sub-factor graphs. Assuming there are F sub-factor graphs, this corresponds to F transmissions of the data. Each sub-factor graph contains J prior nodes, J variable nodes (VNs), and Jd. v There are 3 function nodes (FNs), where VNs and FNs represent the transmitted codeword and the received discrete symbol, respectively. The prior node represents the prior probability of the codeword. The edges in 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 during the iterative update process, the reasonable initial value setting has a significant impact on the detection performance. In traditional initialization methods, constant values ​​are usually assigned to variable nodes and functional nodes. Under this initialization method, neither the detection nor the decoding method takes into account prior information. Therefore, it can lead to poor detector performance or a high bit error rate in the decoded data. In this embodiment, the variable nodes and functional nodes are initialized based on prior information before iterative updates are performed, 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 Therefore, at the sending end, the bit information sequence u of each user is... j First, channel coding is performed, followed by π j After interleaving, SCMA encoding is performed, and then each SCMA symbol is processed by a pulse shaping function to obtain the transmitted symbol.

[0077] At the receiving end, the transmitted symbol sequence is received on each orthogonal resource. After being filtered by the matched filter oriented towards each user, the symbol sequence Y is obtained. When performing detection and decoding for each user based on Y, the detector is executed first, i.e., the proposed P-EPA algorithm.

[0078] P-EPA outputs soft information after the test is completed. After deinterleaving, the data is sent to the soft-output decoder, which outputs the soft information after decoding is complete. After interleaving, the data is sent to the detector. The decoding and detection processes are performed iteratively. The iteration stops when all users get the same result in two consecutive tests, and the estimated information bit sequence is output.

[0079] In one feasible implementation, step S20, which iteratively decodes and detects the symbol sequence data using a preset expectation propagation algorithm and a soft output decoder, and outputs bit sequence information after reaching a preset stopping condition, includes:

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

[0081] It should be noted that the preset expectation propagation algorithm is the detection algorithm in the P-EPA detector, and the steps for iteratively updating the symbol sequence data can be as follows:

[0082] The functional nodes and variable nodes are initialized using prior probabilities. Then, the transmitted codewords and received discrete symbols corresponding to the two nodes are iteratively updated. Finally, before inputting the updated data into the decoder, the extrinsic 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 (LLR) is a key component of the iterative decoding algorithm. It provides a more accurate probability estimate by comprehensively considering information from other bits. With the help of the LLR, the decoder can not only effectively correct random errors but also adapt to different channel conditions and coding schemes, ensuring efficient and reliable data transmission.

[0084] The external information log-likelihood ratio is input to a soft-output decoder. Based on the soft-output decoder, the external information log-likelihood ratio is decoded to obtain soft information.

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

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

[0087] 1. Receive External Information (LLR): Receive each bit of external information (LLR) from Multi-User Detection (MUD) or other front-end processing modules.

[0088] 2. Initialization: The LLR initializes the variable node based on the received external information.

[0089] 3. Iterative decoding:

[0090] 1) Message passing: The Belief Propagation (BP) algorithm is executed on the Factor Graph to update the probability estimate of each bit through message passing between the check node and the variable node.

[0091] 2) External information update: In each iteration, calculate and update the external information LLR for each variable node, taking into account 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 combination with other decoding algorithms.

[0095] The soft information is iteratively exchanged between the detector and the soft output decoder corresponding to the preset expected propagation algorithm until a preset stopping condition is met, and then the 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 terminates. At this point, the final estimated information sequence, i.e., bit sequence information, is output.

[0097] In one feasible implementation, 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 encoding length of the symbol sequence data is less than a preset length threshold, then the soft output decoder algorithm based on the ordered likelihood decoder decodes the log-likelihood ratio of the extrinsic information to obtain soft information.

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

[0100] It should be noted that the preset length threshold can be a preset coding length value, used to measure whether the channel coding is long or short. If the channel coding uses short LDPC codes, the S-OLD algorithm corresponding to the soft output decoder mentioned above can be used. If long LDPC codes are used, the belief propagation algorithm can be used. The decoder outputs soft information after decoding. After interleaving, the data is sent to the detector. The decoding and detection processes are executed iteratively to obtain soft information.

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

[0102] This application proposes a detection and decoding method, apparatus, storage medium, and computer program product based on SCMA. This application receives symbol sequence data corresponding to multiple users, and then iteratively decodes and detects the symbol sequence data using a preset expectation propagation algorithm and a soft-output decoder. The variable nodes and functional nodes in the symbol sequence data are iteratively updated based on prior information, which can ensure improved bit error rate performance of the detector during sequence detection. Furthermore, under asynchronous transmission conditions, it reduces the latency generated during data transmission, lowering the bit error rate and enhancing reception reliability compared to other detection and decoding schemes.

[0103] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 3 Before the step of iteratively updating the symbol sequence data using a preset expectation propagation algorithm to obtain the extrinsic information log-likelihood ratio, the method further includes:

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

[0105] The extended factor graph includes multiple sub-factor graphs corresponding to the number of transmissions. Each sub-factor graph includes multiple prior nodes, variable nodes, and functional 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 functional nodes represent the received discrete symbols.

[0106] It should be noted that, due to the asynchronous nature of the symbols, Interference was caused by symbols transmitted by other users within two consecutive symbol periods; the interfering symbols were... and Based on the above interference relationships, a corresponding extended factor graph was constructed to characterize the interference in the entire frame.

[0107] It should be noted that, as Figure 4 As shown, the extended factor graph consists of F sub-factor graphs, corresponding to F transmissions. Each sub-factor graph contains J prior nodes, J variable nodes (VNs), and Jd. vThere are three function nodes (FNs). VNs and FNs represent the transmitted codeword and the received discrete symbol, respectively. Prior nodes represent the prior probability of the codeword. Edges within the sub-factor graph represent information transfer between VNs and FNs, and edges connecting sub-factor graphs represent information transfer between sub-factor graphs.

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

[0109] Based on the prior node, variable node, and functional node, determine the posterior probability corresponding to the symbol sequence data, the first transmission information from the variable node to the functional node, and the second transmission information from the functional node to the variable node.

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

[0111] Based on the first and / or second transmission information, the information is converted into Gaussian distribution information to obtain first distribution information and second distribution information;

[0112] It should be noted that, Indicates from the prior node The prior information received. If the decoder does not receive any information, then the prior information for all transmitted codewords is the same, that is... Expectation propagation transforms complex posterior distributions into simpler ones through distribution projection.

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

[0114]

[0115]

[0116] Among them and These refer to the first distribution information and the second distribution information, respectively.

[0117] The first distribution information and the second distribution information are iteratively calculated to obtain the updated first transmission information and the updated second transmission information.

[0118] It should be noted that the iterative calculation steps for the first distribution information and the second distribution information can be as follows:

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

[0120] Reasonable initial value and This has a significant impact on detection performance. Traditional initialization methods typically assign constant values ​​to these parameters. However, these methods do not consider prior information. This application proposes a novel initialization method to improve detector performance, the key being... and The initialization is calculated using prior probabilities, and can be represented as follows:

[0121]

[0122] After initialization, the information updates of FNs and VNs are performed iteratively.

[0123] 2. FNs updated;

[0124] Discrete symbols User m, m∈φ k \j Transmission of symbols and noise interference. Assume and This has been obtained from previous iterations. 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 follows:

[0132]

[0133]

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

[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 can be the updated transmission codeword, and the updated second transmission information can be the updated discrete symbol.

[0138] Based on the updated first and second transmission information, the external information log-likelihood ratio is determined.

[0139] It should be noted that determining the extrinsic log-likelihood ratio based on the updated first transitive information (prior information LLR) and the updated second transitive information (variable node message) is an important means to improve decoding performance. By accurately calculating and iteratively updating the extrinsic 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 functional node information are initialized and iteratively updated based on prior probabilities, thereby improving the performance of the detector.

[0141] Based on the first and / or second embodiments of this application, in the third embodiment of this application, the content that is the same as or similar to that in embodiments one and two above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 5 The step of decoding the log-likelihood ratio of the extrinsic information based on the soft output decoder to obtain soft information includes:

[0142] Step S110: Based on the soft output decoder, the external information log-likelihood ratio is split into a first estimated bit sequence and a second estimated bit sequence;

[0143] It should be noted that, Let represent the posterior LLRs obtained by the j-th user from the decoder, which can be calculated according to the Max-Log-MAP criterion as follows:

[0144]

[0145] make yes The hard decision result. In S-OLD, the extrinsic information LLR obtained by the decoder from the nth coded bit can be expressed as:

[0146]

[0147] in, It is C j (n:b) and The weighted Hamming distance (WHD), c j (n:b) is and There is an estimated coded bit sequence with minimum WHD and its nth bit is b, where b = 0, 1. However, directly obtaining... That's too complicated. Below is a lower-complexity method to obtain... The approximate result.

[0148] It should be noted that in the decoder, in order to find the optimal estimated 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, thus yielding a certain number of estimated coded bit sequences and their WHDs. Let... and These represent the decoding process. and The minimum value. Then, construct two vector first estimated bit sequences. Second estimated bit sequence Initialized to,

[0150] Step S120: Decode 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 during OLD (Optimized Likelihood Decoding), a certain number of trial error paths (TEPs) are indeed generated according to a specific strategy to find the optimal estimated codeword sequence. These TEPs are generated in an approximately increasing order of their likelihood, aiming to improve decoding performance by progressively approximating the most likely transmitted codeword. This method combines soft-decision information and iterative search techniques to achieve efficient and accurate decoding.

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

[0153] Decoding stops when the generated first estimated bit sequence and second estimated bit sequence reach the preset stopping condition, and bit sequence information is output.

[0154] The preset stopping conditions include: satisfying the stopping criteria of the soft output decoder, the elements corresponding to the least reliable basis of both the first estimated bit sequence and the second estimated bit sequence have been updated, and the elements corresponding to the most reliable basis of both the first estimated bit sequence and the second estimated bit sequence have not been updated.

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

[0156] In iterative detection and decoding, extrinsic information LLR The prior probability of transforming into an 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 q-th bit of the mapped bit sequence.

[0161] It should be noted that, combining the P-EPA and S-OLD approaches described above, the proposed detection and decoding scheme iteratively exchanges information between the detector and decoder. Let To and Ts represent the maximum number of iterations for the outer iteration and the SCMA detector, respectively. The proposed AIDD (Asynchronous Assisted Iterative Detection and Decoding Scheme) will terminate when To is reached or the decoding results of all users converge. At the To-th iteration of the outer iteration, the decoding result of the j-th user is... Represented as:

[0162]

[0163] The proposed AIDD solution Execution will stop at this point. The corresponding estimated information sequence... From get.

[0164] In this embodiment, the external information likelihood ratio is used for decoding by a soft output decoder, which generates highly reliable soft information compared to existing decoders, thereby improving the error performance of the overall system.

[0165] To demonstrate the effectiveness of the proposed initialization method, the average bit error rate (BER) performance of the proposed P-EPA was first analyzed. For comparison, synchronous MPA and asynchronous original EPA were also simulated, with all schemes having a maximum of 6 iterations. Furthermore, F=64 was set, and two different codebooks with M=4 were used in the simulation, such as the Huawei codebook and the Rayleigh codebook. Figure 6 As shown, asynchronous SCMA outperforms synchronous SCMA at low signal-to-noise ratios. The proposed P-EPA outperforms the original EPA on the Huawei codebook. For the Rayleigh codebook, the proposed P-EPA also outperforms the original EPA and exhibits no error planes. Furthermore, under the same simulation conditions described above, the average detection time per frame for both the proposed P-EPA and the original EPA was statistically analyzed using MATLAB on a 3.0 GHz CPU. As shown in Table 1, the proposed P-EPA has a significantly shorter detection time than 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. Comparison schemes include the asynchronous SCMA independent detection and decoding scheme (individual), synchronous IDD (iterative detection and decoding), synchronous JDD (JDD / MPA) based on MPA (message passing algorithm) (joint detection and decoding), synchronous JDD (JDD / EPA) based on approximate EPA, and a single-user scheme. Note that the single-user scheme is implemented via AIDD, which assumes the receiver is fully aware of the other users' transmitted signals. Therefore, 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 for the individual scheme is set to 9, and the maximum number of iterations for JDD / MPA and JDD / EPA are both set to T = 6, 9.

[0169] Compared to the individual scheme, IDD, JDD / MPA, and JDD / EPA, the proposed AIDD scheme exhibits the best performance, achieving gains of approximately 1.6 dB, 3.4 dB, 3.6 dB, and 6.2 dB respectively at an average BER of 10⁻⁴. Furthermore, the proposed AIDD scheme can approach the performance of a single-user scheme. Additionally, as... Figure 8 As shown in the figure, the average number of external iterations for the synchronous IDD and proposed AIDD schemes, as well as the equivalent average number of iterations for the two JDD schemes, were also analyzed. It can be seen from the figure that, under low signal-to-noise ratio conditions, the average number of iterations for the AIDD algorithm proposed in this application is much less than other schemes, and it converges to 2 iterations as the signal-to-noise ratio increases.

[0170] Finally, the decoder in the Asynchronous Assisted Iterative Detection and Decoding (AIDD) scheme is replaced with the belief propagation (BP) decoding algorithm for LDPC codes, and the performance of the proposed AIDD scheme under long LDPC codes is evaluated. The LDPC code used is Wimax LDPC. C(1056,792) The comparison schemes include the asynchronous SCMA independent detection and decoding scheme (individual), synchronous IDD, and MPA-based synchronous JDD (JDD / MPA). We set To = 2, 3, Ts = 4 and BP for 4 iterations. For fairness, the maximum number of iterations for the individual scheme is set to 12, and the maximum number of iterations for JDD / MPA and JDD / EPA are both set to T = 8, 12. Figure 9As shown, the proposed AIDD has the best performance compared with JDD / MPA, IDD, and individual schemes, achieving gains of approximately 0.65 dB, 0.75 dB, and 1.30 dB respectively at an average BER of 10⁻⁴.

[0171] In summary, this application proposes an ultra-reliable receiver for SCMA in satellite-to-ground communication, which iteratively exchanges information between the detector and decoder while considering time delay. First, we propose a parallel expectation propagation algorithm based on the spread factor graph (P-EPA) and provide a novel initialization method utilizing prior information to improve detection performance. Then, we propose a soft-output decoder called S-OLD to efficiently generate soft information, which serves as the input to 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 advantages such as short detection time and good error performance, which are of great significance for the design of SCMA receivers in satellite-to-ground communication.

[0172] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the detection and decoding based on SCMA in this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0173] This application also provides an SCMA-based detection and decoding device, please refer to... Figure 10 The SCMA-based detection and decoding device includes:

[0174] Receiver module 10 is used 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 using a preset expectation propagation algorithm and a soft output decoder. After reaching a preset stopping condition, it outputs bit sequence information. The symbol sequence data includes multiple variable nodes and multiple functional nodes, and the variable nodes and the functional nodes are iteratively updated based on prior information.

[0176] Optionally, the decoding module includes:

[0177] The processing unit is used to iteratively update the symbol sequence data using a preset expectation propagation algorithm to obtain the external information log-likelihood ratio;

[0178] The decoding processing unit is used to input the external information log-likelihood ratio to the 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 and the soft output decoder corresponding to the preset expected propagation algorithm until a preset stopping condition is reached, and then output the bit sequence information.

[0180] Optionally, the decoding module further includes:

[0181] A construction unit is used to construct an expansion factor graph based on the asynchronous interference relationship between the symbol sequence data.

[0182] The extended factor graph includes multiple sub-factor graphs corresponding to the number of transmissions. Each sub-factor graph includes multiple prior nodes, variable nodes, and functional 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 functional nodes represent the received discrete symbols.

[0183] Optionally, the processing unit includes:

[0184] The first determining subunit is used to determine the posterior probability corresponding to the symbol sequence data, the first transmission information from the variable node to the functional node, and the second transmission information from the functional node to the variable node based on the prior node, the variable node, and the functional node.

[0185] The conversion subunit is used to convert the first transmitted information and / or the second transmitted information into Gaussian distribution information to obtain the first distribution information and the second distribution information;

[0186] An iterative calculation subunit is used to perform iterative calculations on the first distribution information and the second distribution information respectively to obtain updated first transmission information and updated second transmission information;

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

[0188] Optionally, the decoding processing unit includes:

[0189] The splitting subunit is used to split the external 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 decode 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] The output unit is used to stop decoding and output bit sequence information when the generated first estimated bit sequence and second estimated bit sequence reach a preset stopping condition;

[0193] The preset stopping conditions include: satisfying the stopping criteria of the soft output decoder, the elements corresponding to the least reliable basis of both the first estimated bit sequence and the second estimated bit sequence have been updated, and the elements corresponding to the most reliable basis of both the first estimated bit sequence and the second estimated bit sequence have not been updated.

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

[0195] The first processing subunit is configured to decode the log-likelihood ratio of the extrinsic information based on the soft output decoder algorithm of the ordered likelihood decoder to obtain soft information if the encoded length of the symbol sequence data is less than a preset length threshold.

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

[0197] The SCMA-based detection and decoding apparatus provided in this application, employing the SCMA-based detection and decoding methods described in the above embodiments, can solve the technical problems associated with SCMA-based detection and decoding. Compared with the prior art, the beneficial effects of the SCMA-based detection and decoding apparatus provided in this application are the same as those of the SCMA-based detection and decoding methods provided in the above embodiments, and other technical features in the SCMA-based detection and decoding apparatus are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0198] This application provides an SCMA-based detection and decoding device, which 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, which are executed by the at least one processor to enable the at least one processor to perform the SCMA-based detection and decoding described in Embodiment 1 above.

[0199] The following is for reference. Figure 11This document illustrates a structural diagram of an SCMA-based detection and decoding device suitable for implementing embodiments of this application. The SCMA-based detection and decoding device in these embodiments may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), and in-vehicle terminals (e.g., in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Figure 11 The SCMA-based detection and decoding device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0200] like Figure 11 As shown, the SCMA-based detection and decoding device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.) that can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 1002 or a program loaded from storage device 1003 into random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the SCMA-based detection and decoding device. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected 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 I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows the SCMA-based detection and decoding device to exchange data wirelessly or via wired communication with other devices. Although the figure shows SCMA-based detection and decoding devices with various systems, it should be understood that it is not required to implement or possess all of the systems shown. More or fewer systems can be implemented alternatively.

[0201] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0202] The SCMA-based detection and decoding device provided in this application, employing the SCMA-based detection and decoding method described in the above embodiments, 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 this application are the same as those of the SCMA-based detection and decoding method provided in the above embodiments, and other technical features in this SCMA-based detection and decoding device are the same as those disclosed in the method of the previous embodiment, and will not be repeated here.

[0203] It should be understood that the various parts disclosed in this application can be implemented using 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 suitable manner in one or more embodiments or examples.

[0204] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0205] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the SCMA-based detection and decoding method in the above embodiments.

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

[0207] The aforementioned computer-readable storage medium may be included in an SCMA-based detection and decoding device; or it may exist independently and not assembled into an SCMA-based detection and decoding device.

[0208] The aforementioned computer-readable storage medium carries one or more programs that, when executed by the SCMA-based detection and decoding device, cause the SCMA-based detection and decoding device to:

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

[0210] The symbol sequence data is iteratively decoded and detected using a preset expectation propagation algorithm and a soft output decoder. After reaching a preset stopping condition, bit sequence information is output. The symbol sequence data includes multiple variable nodes and multiple functional nodes, which are iteratively updated based on prior information.

[0211] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0212] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0213] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

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

[0215] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the SCMA-based detection and decoding steps described above.

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

[0217] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A detection and decoding method based on SCMA, characterized in that, Applied to the receiving end, the method includes: Receive symbol sequence data corresponding to multiple users; The symbol sequence data is iteratively decoded and detected using a preset expectation propagation algorithm and a soft output decoder. After reaching a preset stopping condition, bit sequence information is output. The symbol sequence data includes multiple variable nodes and multiple functional nodes. The iterative decoding and detection of the symbol sequence data is based on an expansion factor graph. The expansion factor graph is constructed by depicting the asynchronous interference relationship between each symbol sequence data and includes multiple sub-factor graphs corresponding to the number of transmissions. The variable nodes and the functional nodes are iteratively updated based on prior information, and the variable nodes and functional nodes are initialized based on the prior information before the iteration begins.

2. The method as described in claim 1, characterized in that, The step of iteratively decoding and detecting the symbol sequence data using a preset expectation propagation algorithm and a soft output decoder, and outputting bit sequence information after reaching a preset stopping condition, includes: The symbol sequence data is iteratively updated using a preset expectation propagation algorithm to obtain the external information log-likelihood ratio. The external information log-likelihood ratio is input to a soft-output decoder. Based on the soft-output decoder, the external information log-likelihood ratio is decoded to obtain soft information. The soft information is iteratively exchanged between the detector and the soft output decoder corresponding to the preset expected propagation algorithm until a preset stopping condition is met, and then the bit sequence information is output.

3. The method as described in claim 2, characterized in that, Before the step of iteratively updating the symbol sequence data using a preset expectation propagation algorithm to obtain the external information log-likelihood ratio, the method further includes: Based on the asynchronous interference relationship between the symbol sequence data, an expansion factor graph is constructed; The extended factor graph includes multiple sub-factor graphs corresponding to the number of transmissions. Each sub-factor graph includes multiple prior nodes, variable nodes, and functional 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 functional nodes represent the received discrete symbols.

4. The method as described in claim 3, characterized in that, The step of iteratively updating the symbol sequence data using a preset expectation propagation algorithm to obtain the external information log-likelihood ratio includes: Based on the prior node, variable node, and functional node, determine the posterior probability corresponding to the symbol sequence data, the first transmission information from the variable node to the functional node, and the second transmission information from the functional node to the variable node. Based on the first and / or second transmission information, the information is converted into Gaussian distribution information to obtain first distribution information and second distribution information; The first distribution information and the second distribution information are iteratively calculated to obtain the updated first transmission information and the updated second transmission information. Based on the updated first and second transmission information, the external information log-likelihood ratio is determined.

5. The method as described in 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 includes: Based on the soft-output decoder, the external information log-likelihood ratio is split 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 as described in claim 5, characterized in that, The step of outputting bit sequence information after reaching a preset stopping condition includes: Decoding stops when the generated first estimated bit sequence and second estimated bit sequence reach a preset stopping condition, and the output bit sequence is generated. The preset stopping condition includes: satisfying the stopping criterion of the soft output decoder; the elements corresponding to the least reliable basis of both the first estimated bit sequence and the second estimated bit sequence have been updated; and the elements corresponding to the most reliable basis of both the first estimated bit sequence and the second estimated bit sequence have not been updated.

7. The method as described in 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 encoding length of the symbol sequence data is less than a preset length threshold, then the soft output decoder algorithm based on the ordered likelihood decoder decodes the log-likelihood ratio of the extrinsic information to obtain soft information. If the encoded length of the symbol sequence data is greater than or equal to a preset length threshold, then based on the belief propagation algorithm, the log-likelihood ratio of the extrinsic information is decoded to obtain soft information.

8. A detection and decoding device based on SCMA, characterized in that, The device includes: The receiving module is used to receive symbol sequence data corresponding to multiple users; The decoding module is used to iteratively decode and detect the symbol sequence data using a preset expected propagation algorithm and a soft output decoder. After reaching a preset stopping condition, it outputs bit sequence information. The symbol sequence data includes multiple variable nodes and multiple functional nodes. The iterative decoding and detection of the symbol sequence data is based on an expansion factor graph. The expansion factor graph is constructed by depicting the asynchronous interference relationship between each symbol sequence data and includes multiple sub-factor graphs corresponding to the number of transmissions. The variable nodes and the functional nodes are iteratively updated based on prior information, and the variable nodes and functional nodes are initialized based on the prior information before the iteration begins.

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, it implements the steps of SCMA-based detection and decoding as described in any one of claims 1 to 7.

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

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