Switching-Cost-Aware Deep Reinforcement Learning for Dynamic Port Selection in Fluid Antenna Systems

Fluid antenna systems (FAS) have emerged as a promising enabling technology for sixth-generation (6G) wireless networks. By dynamically switching ports according to channel characteristics, FAS can exploit spatial diversity and significantly enhance wireless performance. However, most existing studies focus on maximizing instantaneous throughput while neglecting the long-term cost associated with frequent port switching. This work aims to jointly optimize long-term throughput and port-switching cost in FAS, considering spatio-temporally correlated channels and minimum-rate requirements for each user. To address this challenge, we formulate the dynamic port selection problem as a Markov decision process (MDP) and employ a dueling DQN framework to obtain a near-optimal policy. A Transformer encoder is incorporated to capture spatial-temporal dependencies among ports, and a candidate pre-selection mechanism is introduced to reduce training complexity. Simulation results show that the proposed method achieves up to 96% of the per-slot exhaustive-search optimum while significantly reducing decision latency, demonstrating high efficiency and scalability for large-scale FAS deployments.