Joint Computing Resource, Power, and Channel Allocations for D2D-Assisted and NOMA-Based Mobile Edge Computing

• Published in: IEEE access 2019, Vol.7, p.9243-9257
• Main Authors: Diao, Xianbang, Zheng, Jianchao, Wu, Yuan, Cai, Yueming
• Format: Article
• Language: English
• Subjects: Adaptive algorithms; Algorithms; and channel allocation; Channel allocation; device-to-device (D2D) communications; Device-to-device communication; Edge computing; Energy consumption; Matching; Mobile computing; Mobile edge computing (MEC); NOMA; non-orthogonal multiple access (NOMA); Nonorthogonal multiple access; Particle swarm optimization; power allocation; Power management; Resource allocation; Resource management; Resource scheduling; Servers; Task analysis
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2018.2890559

Mobile edge computing (MEC) and non-orthogonal multiple access (NOMA) have been considered as the promising techniques to address the explosively growing computation-intensive applications and accomplish the requirement of massive connectivity in the fifth-generation networks. Moreover, since the computing resources of the edge server are limited, the computing load of the edge server needs to be effectively alleviated. In this paper, by exploiting device-to-device (D2D) communication for enabling user collaboration and reducing the edge server's load, we investigate the D2D-assisted and NOMA-based MEC system. In order to minimize the weighted sum of the energy consumption and delay of all users, we jointly optimize the computing resource, power, and channel allocations. Regarding the computing resource allocation, we propose an adaptive algorithm to find the optimal solution. Regarding the power allocation, we present a novel power allocation algorithm based on the particle swarm optimization (PSO) for the single NOMA group comprised of multiple cellular users. Then, for the matching group comprised of a NOMA group and D2D pairs, we theoretically derive the interval of optimal power allocation and propose a PSO-based algorithm to solve it. Regarding the channel allocation, we propose a one-to-one matching algorithm based on the Pareto improvement and swapping operations and extend the one-to-one matching algorithm to a many-to-one matching scenario. Finally, we propose a scheduling-based joint computing resource, power, and channel allocations algorithm to achieve the joint optimization. The simulation results show that the proposed solution can effectively reduce the weighted sum of the energy consumption and delay of all users.