Fluid Antenna-Enabled Hybrid NOMA and AirFL Networks Under Imperfect CSI and SIC

TL;DR

This paper proposes a fluid antenna-enabled hybrid NOMA and AirFL network that adaptively mitigates interference and enhances performance under imperfect CSI and SIC using deep reinforcement learning.

Contribution

It introduces a joint optimization framework for hybrid networks with fluid antennas, addressing practical CSI and SIC uncertainties with a novel LSTM-DDPG solution.

Findings

Fluid antennas improve network performance under uncertainties.

The proposed method outperforms fixed-antenna baselines.

Deep reinforcement learning effectively manages complex optimization.

Abstract

The integration of communication and computation is essential for next-generation wireless systems, especially in scenarios demanding massive connectivity and ultra-low latency. Over-the-air federated learning (AirFL), leveraging the superposition nature of wireless channels, enables fast data aggregation, while non-orthogonal multiple access (NOMA) offers spectrum-efficient connectivity. This paper investigates a fluid antenna (FA)-aided hybrid network, supporting hybrid users comprising both AirFL and NOMA participants. The dynamic reconfigurability of FAs offers significant potential for mitigating interference and enhancing network performance by adapting antenna positions in response to changing channel conditions. We consider practical challenges arising from imperfect channel state information (CSI) and residual interference due to imperfect successive interference cancellation (SIC). To jointly evaluate the learning and communication performance, a hybrid rate metric is introduced. Subsequently, we formulate a robust optimization problem that jointly minimizes the aggregation error while ensuring reliable user communication under CSI and SIC uncertainties. 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 solve this problem effectively, 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. Simulation results demonstrate the superiority of the proposed FA-assisted approach over fixed-antenna baselines, particularly under imperfect CSI and SIC conditions, in terms of hybrid rate performance.