Sparse Code Multiple Access-Based Edge Computing for IoT Systems

Abstract

In this paper, a sparse code multiple access (SCMA)-based edge computing scheme is proposed for Internet-of-Things (IoT) systems. The aim of implementing SCMA, which is a nonorthogonal multiple access resource allocation technique, is to improve network connectivity and maximize data rate provision. The proposed edge-IoT system is investigated under different SCMA configurations to explore the various performance aspects such as connectivity, throughput, task completion time, and complexity. First, the problem is formulated as a data rate maximization problem for SCMA-based heterogeneous networks under power constraints. Then, the problem is subdivided into a power allocation problem, which is solved using the water filling approach, and a codebook allocation problem that is solved using a heuristic algorithm. The results show that the SCMA scheme can significantly improve the IoT performance compared to the conventional orthogonal frequency-division multiple access resource allocation scheme in terms of connectivity, throughput, and task completion time provided that SCMA configurations are suitable with IoT processing capabilities to avoid undesired detection latency.