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The emergence of fluid antenna systems (FAS) offers a novel technique for obtaining spatial diversity and leveraging interference fades for spectrum sharing in multiuser scenarios—a paradigm referred to as fluid antenna multiple access (FAMA). Nevertheless, as the number of users increases, the interference mitigation capability diminishes. To overcome this, opportunistic scheduling that prioritizes robust users proves to be an effective method for enhancing FAMA. This paper introduces a resilient decentralized reinforcement learning (RL) approach for opportunistic FAMA (O-FAMA), to autonomously select robust users and the port of each chosen user’s FAS jointly to maximize the network sum-rate. In order to enhance learning efficiency in this multi-agent environment, we propose a novel team-theoretic RL framework that includes a derivative network guiding the multi-agent learning of each solution’s policy networks. Our simulation results confirm the effectiveness of the proposed methodology. IEEE

In this paper, we consider a unmanned aerial vehicle (UAV)-enabled multiple-input single-output (MISO) non-orthogonal multiple access (NOMA) system for a smart city environment, where a multi-Antenna UAV acts as an amplify-And-forward (AF) relay to extend the coverage to several disconnected users aided by NOMA. We present an optimization problem that jointly determines the location of the UAV, optimal beamforming at the UAV and power allocation at the base station (BS), in order to maximize the system sum-rate. Due to the severe non-convexity of the problem, we decouple the problem into three sub-problems and solve them sequentially. First, the optimal location of the UAV is determined by minimizing the total path loss. Next, the UAV beamforming is solved by transforming the problem into second-order cone programming (SOCP) and finally the power allocation at the BS is determined using linear-fractional programming (LFP). The simulation results demonstrate that the proposed scheme performs better than the baseline schemes and orthogonal multiple access (OMA) scheme.