A ThresholdBased MaxlogMPA Low Complexity Multiuser Detection Algorithm Sparse Code Multiple Access (SCMA) technology is a new multiple access scheme based on nonorthogonal spread spectrum technology, which was proposed by Huawei in 2014. In the algorithm application of this technology, the original Message Passing Algorithm (MPA) has slow convergence speed and high algorithm complexity. The thresholdbased MPA has a high Bit Error Ratio (BER) when the threshold is low. In the Maximum logarithm Message Passing Algorithm (MaxlogMPA), the approximation method is used, which will cause some messages to be lost and the detection performance to be poor. Therefore, in order to solve the above problems, a ThresholdBased MaxlogMPA (TMaxlogMPA) low complexity multiuser detection algorithm is proposed in this paper. The Maximum logarithm (Maxlog) algorithm is combined with threshold setting, and the stability of user nodes is considered as a necessary condition for decision in the algorithm. Before message updating, the user information nodes are judged whether the necessary conditions for the stability of the user node have been met, and then the threshold is determined. Only users who meet the threshold condition and pass the necessary condition of user node stability can be decoded in advance. In the whole process, the logarithm domain MPA algorithm is used to convert an exp operation and a multiplication operation into a maximum value and addition operation. The simulation results show that the proposed algorithm can effectively reduce the computational complexity while ensuring the BER, and with the increase of signaltonoise ratio, the effect of the Computational Complexity Reduction Ratio (CCRR) is more obvious.

Deep Learning Based Modified Message Passing Algorithm for Sparse Code Multiple Access Shuffled message passing algorithm (SMPA) is a serial variant of message passing algorithm (MPA) for sparse code multiple access (SCMA) signal detection, which accelerates the convergence rate of MPA. However, SMPA still achieves the nearoptimal performance due to the effect of cycles in the factor graph. In the paper, we propose to optimize the weights assigned to the edges of the factor graph by unfolding SMPA as layers of deep neural network (DNN). We consider the weights as network parameters and then train the network offline to obtain weights which can minimize the loss function. With simulations, we show that DNN based SMPA (DNNSMPA) outperforms SMPA in terms of biterrorrate (BER) for the same level of computational complexity.