Energy-Efficient Resource Allocation and Subchannel Assignment for NOMA-Enabled Multiaccess Edge Computing

In this article, we study an energy-efficient nonorthogonal multiple access (NOMA) enabled multiaccess edge computing (MEC) system with strict latency requirements. We aim to minimize the energy consumption of all users by optimizing the resource allocation (including power and computation resources) and subchannel assignment, subject to the given latency constraint. The formulated problem, however, is a nonconvex combinatorial optimization problem. Nevertheless, we decompose the problem into a resource allocation subproblem and a subchannel assignment subproblem, and then solve the two subproblems iteratively. On one hand, we investigate the hidden convexity of the resource allocation subproblem under the optimal conditions, and propose an efficient algorithm to optimally allocate the resources by dual decomposition methods. On the other hand, we formulate the subchannel assignment subproblem into an integer linear programming problem and strictly prove that the problem is nondeterministic polynomial-time hard. We then solve it optimally by branch-and-bound methods, which is shown to be efficient in extensive simulations. Moreover, through considerable simulation results, we show that our proposed algorithm helps greatly reduce users’ energy consumption when communication resources (e.g., bandwidth) are limited. Additionally, it is verified that NOMA outperforms orthogonal multiple access in multiuser latency-sensitive MEC systems.

I. Introduction

The Internet of Things (IoT) has had explosive growth in recent years. Emerging IoT applications, such as autonomous driving, virtual reality/augmented reality and so forth, are latency-sensitive and computation-intensive [1], [2]. However, IoT devices are typically characterized by restricted computation resource. Hence, it is challenging to implement these latency-sensitive applications on such devices, and a significant surge in demand for computation resource is induced [3]. Recently, multiaccess edge computing (MEC) has been considered as a promising solution to tackle these issues [4]. MEC extends the cloud-computation capabilities and IT service environment to the edge of the network. Compared to faraway centralized data centers, MEC servers can be deployed at various access points [e.g., mobile base stations (BSs)] close to IoT devices. By offloading the computation tasks to MEC servers, devices can finish the data delivery and computation with a short latency. MEC systems have been investigated with different optimization objectives and in multifarious situations [5]–[7]. In [5], the joint computation offloading and resource allocation strategy was studied to minimize users’ energy consumption. In [6], latency-minimization problems were formulated and studied in a scenario where users may opt for partial offloading to the edge server via an access point with the aid of an intelligent reflecting surface. In [7], a dynamic spectrum management framework was proposed to improve spectrum resource utilization in MEC systems in autonomous vehicular networks.