An Optimization Scheme for SCMA-Based Multi-Access Edge Computing

Abstract

Sparse code multiple access (SCMA) is a kind of code-domain non-orthogonal multiple access (NOMA) scheme, which can support the increasing requirements for high spectral efficiency and massive connections. Meanwhile, multi-access edge computing (MEC) is a promising technology for providing resource-constrained users with computing resources. The integration of these two technologies can efficiently improve the computation service. In this paper, we propose a novel optimization scheme in the SCMA-based MEC network from the perspective of energy and latency for the Internet of Things (IoT) devices. To minimize the total overhead of devices, the communication and computation resources allocation, as well as computation offloading are jointly considered. Primarily, a joint SCMA codebook assignment based on the bidirectional matching principle and optimal power allocation algorithm is proposed to maximize the uplink transmit rate. Moreover, we put forward CPU frequency scheduling strategies utilizing convex optimization to optimize the computing resources allocation of local devices and the MEC server. Finally, a low-complexity task offloading policy based on simulated annealing is presented. Numerical results show that our proposed joint optimization algorithm for resource allocation and computation offloading can achieve a good compromise between time delay and energy consumption for IoT devices.