Latency and Energy Minimization in NOMA-Assisted MEC Network: A Federated Deep Reinforcement Learning Approach

Abstract

Multi-access edge computing (MEC) is seen as a vital component of forthcoming 6G wireless networks, aiming to support emerging applications that demand high service reliability and low latency. However, ensuring the ultra-reliable and low-latency performance of MEC networks poses a significant challenge due to uncertainties associated with wireless links, constraints imposed by communication and computing resources, and the dynamic nature of network traffic. Enabling ultra-reliable and low-latency MEC mandates efficient load balancing jointly with resource allocation. In this paper, we investigate the joint optimization problem of offloading decisions, computation and communication resource allocation to minimize the expected weighted sum of delivery latency and energy consumption in a non-orthogonal multiple access (NOMA)-assisted MEC network. Given the formulated problem is a mixed-integer non-linear programming (MINLP), a new multi-agent federated deep reinforcement learning (FDRL) solution based on double deep Q-network (DDQN) is developed to efficiently optimize the offloading strategies across the MEC network while accelerating the learning process of the Internet-of-Thing (IoT) devices. Simulation results show that the proposed FDRL scheme can effectively reduce the weighted sum of delivery latency and energy consumption of IoT devices in the MEC network and outperform the baseline approaches.