Research On Multiuser Detection Algorithm In Sparse Code Multiple Access Systems

Under the constraints of spectrum resources,traditional Orthogonal Multiple Access(OMA)technology has been unable to meet the massive user connections,ultra-low latency and ultra-high system of the Fifth Generation communication system.Key requirements such as capacity.Sparse Code Multiple Access technology is a new type of non-orthogonal multiple access technology based on multi-dimensional modulation and sparse code spreading.It solves the problems of supporting massive user connections and ultra-high system capacity.Message Passing Algorithm is the classic detection algorithm of SCMA system.The algorithm uses the characteristics of sparse codes to detect through the iterative calculation of information between resource nodes and user nodes.The performance is close to the optimal maximum.The performance of the Maximum A Posterior algorithm.Since the computational complexity of the MPA algorithm has an exponential function relationship with the number of users connected in each physical resource,and there are a large number of exponential multiplication operations in the iterative process,it is not conducive to the application of actual communication scenarios.In order to promote the wider use of SCMA technology,this paper studies efficient message passing algorithm detection schemes for non-coding SCMA systems and Turbo code-based SCMA systems.The main work arrangements are as follows:In the non-coding SCMA system,this thesis mainly optimizes the MPA algorithm from two aspects,which reduces the computational complexity of the algorithm and speeds up the iteration speed of the algorithm.On the one hand,it proposes an adaptive factor graph message passing algorithm(AFG-MPA)from the perspective of factor graphs.By calculating the confidence of each node in the factor graph,it can adaptively perform converged edges.The adjustment avoids the convergent edges in the factor graph from continuing to participate in subsequent iterations,and reduces the computational complexity of the algorithm.On the other hand,it analyzes the difference between serial thought and parallel thought.Based on the research of AFG-MPA algorithm,the serial thought and AFG-MPA algorithm are further combined,and a serial message passing algorithm based on adaptive factor graph(S-AFG-MPA)is studied.this algorithm accelerates the convergence speed of the algorithm based on the AFG-MPA algorithm,and is more suitable for 5G large-scale terminal access scenarios.In the Turbo code-based SCMA system,by combining the Turbo code with the SCMA system,a new message passing algorithm based on Turbo code iterative decoding(ID-MPA)feedback is proposed.The ID-MPA algorithm calculates the prior probability information of the code by calculating the log-likelihood ratio information of the decoded bits,and then feeds back the prior probability information of the code to the MPA detector.As the initial value of the user section information update in the next iteration,the detection performance of the SCMA system is improved.In addition,this thesis optimizes the ID-MPA algorithm from two aspects.On the one hand,for the problem that the converged code do not need to participate in subsequent iterations,an iterative decoding feedback message passing algorithm based on code selection(CS-ID-MPA)is studied.This algorithm only In the first outer loop,all codes are subjected to inner loop iteration detection,and in all subsequent outer loops,it is first judged whether the code needs inner loop iteration.If the inner loop iteration is not needed,the code is skipped directly,otherwise,the same inner loop iteration as the ID-MPA algorithm is performed.By filtering out the code words that have converged,the convergence speed of the algorithm is accelerated.On the other hand,an iterative decoding feedback message passing algorithm based on factor graph reuse(FGR-ID-MPA)is studied from the perspective of factor graphs.The message difference between the last two iterations of the user node in the loop is corrected by the factor graph.In the next outer loop iteration,the factor graph corrected by the last outer loop iteration is used,which reduces the iteration of unnecessary edges in the factor graph,which reduces the computational complexity of the algorithm.