Research on sparse codebook multiple access technology based on generated adversarial network

Sparse code book multiple access (SCMA), due to its good link performance, adapt to large-scale access scenarios, helps to cope with the surge of 5G and next generation mobile communication equipment and the challenge of access capacity improvement. The implementation of SCMA system faces two major challenges: optimal codebook design and efficient decoding algorithm. At present, the method based on deep learning has been studied in SCMA system. In this paper, a SCMA system scheme based on generative network introduces Transformer attention mechanism in the encoder and uses context information to solve the problems of high complexity and insufficient flexibility. In the decoder, Patch GAN is adopted to reduce the number of parameters and calculation of network model, and solve the problem of high complexity of traditional decoding algorithm to improve the performance of code error. The research of SCMA system based on generated adversarial network has important theoretical significance and practical value for the selection of multiple access scheme of 5G and future mobile communication system.