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

TL;DR

This paper proposes a DRL-based user grouping and resource allocation scheme for hybrid NOMA-MEC systems, improving offloading efficiency and energy consumption in 6G networks.

Contribution

It introduces a dynamic hybrid SIC scheme and a DRL algorithm for near-optimal user grouping in NOMA-MEC, enhancing performance over traditional OMA methods.

Findings

The proposed algorithm converges quickly.

NOMA-MEC outperforms OMA in offloading efficiency.

Energy consumption is significantly reduced.

Abstract

Multi-access edge computing (MEC) and non-orthogonal multiple access (NOMA) have been regarded as promising technologies to improve computation capability and offloading efficiency of the mobile devices in the sixth generation (6G) mobile system. This paper mainly focuses on the hybrid NOMA-MEC system, where multiple users are first grouped into pairs, and users in each pair offload their tasks simultaneously by NOMA, and then a dedicated time duration is scheduled to the more delay-tolerable user for uploading the remaining data by orthogonal multiple access (OMA). For the conventional NOMA uplink transmission, successive interference cancellation (SIC) is 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 integrate 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 is proposed to obtain a close-to-optimal user grouping policy. Moreover, we optimally minimize the offloading energy consumption by obtaining the closed-form solution to the resource allocation problem. Simulation results show that the proposed algorithm converges fast, and the NOMA-MEC scheme outperforms the existing orthogonal multiple access (OMA) scheme.