Robust Resource Allocation in RIS-Assisted Wireless Networks Integrating NOMA and Over-the-Air Federated Learning

TL;DR

This paper proposes a RIS-assisted wireless network framework integrating NOMA and over-the-air federated learning, optimized via deep reinforcement learning to improve performance amid channel uncertainties.

Contribution

It introduces a novel joint optimization framework for RIS, NOMA, and AirFL, solved with a deep reinforcement learning algorithm to enhance robustness and convergence.

Findings

Faster convergence and lower variance compared to baseline algorithms.

Improved robustness under channel uncertainty.

Enhanced network performance for communication and learning tasks.

Abstract

This paper addresses the critical issue of spectrum scarcity and the need to support diverse services, including communication and learning tasks, by presenting a reconfigurable intelligent surface (RIS)-aided wireless network framework that integrates non-orthogonal multiple access (NOMA) with over-the-air federated learning (AirFL). The proposed system leverages the ability of RIS to adaptively shape wireless channels, aiming to enhance overall network performance for both communication and learning through concurrent uplink transmissions. To tackle critical challenges such as co-channel interference, imperfect channel state information (CSI), and successive interference cancellation (SIC), we develop an optimization framework that focuses on minimizing the optimality gap. This joint optimization is formulated as a non-convex problem, complicated by the intricate interactions between NOMA and AirFL users as well as the impact of imperfect CSI and SIC. To overcome these challenges and reduce the optimality gap, we reformulate the optimization problem as a Markov decision process and solve it using a long short-term memory deep deterministic policy gradient (LSTM-DDPG) algorithm, a memory-based approach within deep reinforcement learning (DRL). Simulation results demonstrate that the proposed approach achieves faster convergence, lower variance, and improved robustness under channel uncertainty, outperforming baseline DRL algorithms such as DDPG, soft actor-critic (SAC), and advantage actor-critic (A2C).