A nature-inspired codebook design strategy for uplink sparse code multiple access system

As a type of coding-based non-orthogonal multiple access technology, sparse code multiple access (SCMA) has been drawing attention for its undeniable potential to be utilized in sixth-generation (6G) and beyond telecommunication systems. The key advantage of SCMA comes from combining the spread spectrum and modulation procedures. The joint usage of these procedures leads to a considerable enhancement of active users in a certain time–frequency resource. On the other hand, each user is assigned a unique codebook to separate the multiple users from each other. For this reason, the performance of SCMA is directly affected by the design of codebook set. Codebook design is a critical issue requiring efficient solutions in the SCMA system. The primary objective in designing the codebooks is to discover the ideal codebook set that makes the SCMA to reach the maximum performance. In this paper, an efficient nature-inspired codebook design strategy, in which the whale optimization algorithm (WOA) is utilized as a codebook optimizer, is proposed for the uplink SCMA system. In this strategy, unlike most methods that use key performance indicators like minimum Euclidean distance (MED) or minimum product distance (MPD) of the superimposed codewords as a design criterion, we use bit error rate (BER) of the SCMA system as an objective function to be minimized by the WOA algorithm. By doing so, instead of focusing only on the superimposed codewords, all parts of the system affecting the BER performance are taken into consideration during the optimization process. In the simulations, our proposed WOA-based strategy is compared to various classical and metaheuristic-based codebook design methods for both additive white Gaussian noise (AWGN) and Rayleigh channel models. Compared to the other techniques, the proposed codebook optimization procedure can produce a better codebook set allowing the uplink SCMA to reach lower BER levels for each of the considered channel models.