AN OPTIMIZATION ALGORITHM FOR SPARSE CODE MULTIPLE ACCESS CODEBOOK TO MAXIMIZE MUTUAL INFORMATION IN 5G

Abstract

Sparse code multiple access (SCMA) codebook optimization algorithms strive to reduce complexity with negligible loss at the transmitting end but add more complexity at the receiver, such that the retrieved information is minimal. To optimize mutual information in 5G using SCMA, an efficient codebook optimization algorithm is presented. The SCMA signal model is considered first using a superposition modulation structure with a column expanded channel matrix. The superposition model accurately describes the link between the received signal and the codebook matrix. An iterative codebook optimization approach based on the above model is suggested to maximize (continuous output and discrete input) mutual information. This method effectively adapts random channel coefficients to multiuser channels; both additive white Gaussian noise (AWGN) and non-AWGN channels are available to perform well with the proposed technique, according to simulation findings. Furthermore, the proposed optimization algorithm for SCMA codebook works well with the message passing algorithm.