DRL-Assisted Resource Allocation for NOMA-MEC Offloading with Hybrid SIC

Li, Haodong; Fang, Fang; Ding, Zhiguo

Abstract

Multi-access edge computing (MEC) and non-orthogonal multiple access (NOMA) are regarded as promising technologies to improve the computation capability and offloading efficiency of mobile devices in the sixth-generation (6G) mobile system. This paper mainly focused on the hybrid NOMA-MEC system, where multiple users were first grouped into pairs, and users in each pair offloaded their tasks simultaneously by NOMA, then a dedicated time duration was scheduled to the more delay-tolerant user for uploading the remaining data by orthogonal multiple access (OMA). For the conventional NOMA uplink transmission, successive interference cancellation (SIC) was applied to decode the superposed signals successively according to the channel state information (CSI) or the quality of service (QoS) requirement. In this work, we integrated the hybrid SIC scheme, which dynamically adapts the SIC decoding order among all NOMA groups. To solve the user grouping problem, a deep reinforcement learning (DRL)-based algorithm was proposed to obtain a close-to-optimal user grouping policy. Moreover, we optimally minimized the offloading energy consumption by obtaining the closed-form solution to the resource allocation problem. Simulation results showed that the proposed algorithm converged fast, and the NOMA-MEC scheme outperformed the existing orthogonal multiple access (OMA) scheme

Citation

Li, H., Fang, F., & Ding, Z. (2021). DRL-Assisted Resource Allocation for NOMA-MEC Offloading with Hybrid SIC. Entropy, 23(5), Article 613. https://doi.org/10.3390/e23050613

• Journal Article Type: Article
• Acceptance Date: May 11, 2021
• Online Publication Date: May 14, 2021
• Publication Date: 2021
• Deposit Date: May 20, 2021
• Publicly Available Date: Nov 3, 2021
• Journal: Entropy
• Electronic ISSN: 1099-4300
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 23
• Issue: 5
• Article Number: 613
• DOI: https://doi.org/10.3390/e23050613
• Public URL: https://durham-repository.worktribe.com/output/1242341