Joint Task Offloading and Resource Management in NOMA-Based MEC Systems: A Swarm Intelligence Approach

Abstract

In this paper, we study the issue of computation offloading in non-orthogonal multiple access (NOMA)-based multi-access edge computing (MEC) systems. A joint optimization problem of offloading decision, subchannel assignment, transmit power, and computing resource allocation is investigated to improve system performance in terms of both completion time and energy consumption. The formulated problem is a mixed-integer non-linear programming one, it is therefore hard to solve. To make the problem tractable, we first decompose the problem into subproblems of computing resource allocation (CRA), transmit power control (TPC), and subchannel assignment (SA). Then, we address the CRA subproblem by a convex optimization technique. For the remaining two subproblems TPC and SA, we propose to use a gradient-free swarm intelligence approach, namely whale optimization algorithm, to provide a very general but efficient solution. Computer simulations are performed to show the convergence of the proposed algorithm, also its better performance in comparison with conventional schemes.

Collective Intelligence Using 5G: Concepts, Applications, and Challenges in Sociotechnical Environments

“…Advanced multiple access techniques such as non-orthogonal multiple access (NOMA) and rate-splitting multiple access (RSMA) are being considered for boosting the connectivity capacity of 6G networks and supporting super-overloaded scenarios [59], [60]. In some cases, solutions based on some form of collective intelligence, such as swarm intelligence, have been proposed [61], for example, for joint task offloading and resource management in mobile edge computing (MEC) systems that use NOMA [62]. Swarm intelligence and especially machine learning (ML)assisted swarm intelligence is still a nascent topic and a prospective technique that has significant potential to address the problems with existing multiple access technologies, such as connectivity capacity, speed, and fair usage.…”

Joint Computational Offloading and Data-Content Caching in NOMA-MEC Networks

“…They optimized the NOMA to support the decrease in latency in the MEC system. Pham et al [27] maximized task offloading gains by jointly considering computational offloading, resource allocation, sub-carrier assignment, and power control in NOMA-MEC systems. Hao et al [12] introduced a hybrid NOMA-MEC system to enhance the computation service for Sixth-Generation (6G) wireless networks.…”

“…Several studies on NOMA-MEC systems have centered on the issue of computational offloading optimization. The majority of papers attempted to minimize the latency and consumed energy [12], [17]- [27]. On the other hand, most studies on NOMA-MEC systems do not consider the benefits of edge caching.…”

Multi-Objective Whale Optimization Algorithm for Computation Offloading Optimization in Mobile Edge Computing

“…WOA is introduced to solve computation offloading problems by Pham, Huong et al [ 35 ] and Pham, Quoc et al [ 36 ]. Pham, Huong et al employ WOA to solve the Transmit Power Control (TPC) problems and they find the simplicity and efficiency of WOA on TPC problems.…”

“…Second, the whale optimization algorithm is universal for many application areas with the advantages mentioned, and has many successful applications [ 28 , 29 , 30 , 31 , 32 , 33 , 34 ]. Third, WOA is used to solve the optimization strategy of computing offloading [ 35 , 36 ]. However, they only focus on just one factor and ignore the fact that the optimization of computation offloading may be affected by multiple factors.…”