This paper addresses the critical issue of spectrum scarcity and the need to support diverse services, including com- munication 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 RIS’s ability 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 and the impact of imperfect CSI and SIC. To overcome these challenges and reduce the optimality gap, we reformulate the optimization 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 the realm of 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).

Keywords