Energy Efficient Resource Allocation for Mobile-Edge Computation Networks with NOMA

TL;DR

This paper proposes an energy-efficient resource allocation method for uplink NOMA-based mobile-edge computing networks, optimizing energy consumption while satisfying latency and capacity constraints, with a globally optimal iterative algorithm.

Contribution

It introduces a convex optimization framework and a low-complexity iterative algorithm for energy minimization in NOMA-MEC networks, demonstrating global optimality.

Findings

The problem is convex and transmission with maximal time is optimal.

The proposed algorithm outperforms conventional methods.

The method achieves lower energy consumption in simulations.

Abstract

This paper investigates an uplink non-orthogonal multiple access (NOMA)-based mobile-edge computing (MEC) network. Our objective is to minimize the total energy consumption of all users including transmission energy and local computation energy subject to computation latency and cloud computation capacity constraints. We first prove that the total energy minimization problem is a convex problem, and it is optimal to transmit with maximal time. Then, we accordingly proposed an iterative algorithm with low complexity, where closed-form solutions are obtained in each step. The proposed algorithm is successfully shown to be globally optimal. Numerical results show that the proposed algorithm achieves better performance than the conventional methods.