Reconfigurable Intelligent Surface Optimization for Uplink Sparse Code Multiple Access

TL;DR

This paper introduces optimization algorithms for RIS phase shifts in uplink RIS-SCMA systems, significantly enhancing received SNR while reducing computational complexity, and explores RIS deployment strategies.

Contribution

It is the first to optimize RIS phase shifts in uplink RIS-SCMA using AO and low-complexity AO algorithms, improving system performance.

Findings

Proposed AO and LC-AO algorithms improve received SNR.

LC-AO achieves similar SNR as AO with lower complexity.

RIS deployment strategies for uplink RIS-SCMA are analyzed.

Abstract

The reconfigurable intelligent surface (RIS)-assisted sparse code multiple access (RIS-SCMA) is an attractive scheme for future wireless networks. In this letter, for the first time, the RIS phase shifts of the uplink RIS-SCMA system are optimized based on the alternate optimization (AO) technique to improve the received signal-to-noise ratio (SNR) for a discrete set of RIS phase shifts. The system model of the uplink RIS-SCMA is formulated to utilize the AO algorithm. For further reduction in the computational complexity, a low-complexity AO (LC-AO) algorithm is proposed. The complexity analysis of the two proposed algorithms is performed. Monte Carlo simulations and complexity analysis show that the proposed algorithms significantly improve the received SNR compared to the non-optimized RIS-SCMA scenario. The LC-AO provides the same received SNR as the AO algorithm, with a significant reduction in complexity. Moreover, the deployment of RISs for the uplink RIS-SCMA is investigated.