Deep Reinforcement Learning-Assisted Resource Allocation for Fluid Antenna System: Overview, Research Challenges and Future Trends

In recent years, Fluid Antenna Systems (FAS) have emerged as an innovative solution in wireless communications, offering dynamic adaptability by adjusting antenna positions within a confined space to enhance signal reception and coverage. By seamlessly optimizing antenna placement in response to varying channel conditions, FAS significantly improves the signal-to-noise ratio (SNR) and ensures superior quality of service (QoS), particularly in challenging environments. A key aspect of maximizing the potential of FAS lies in efficient resource allocation, which is essential for optimizing network performance, balancing power and latency, and mitigating interference. This paper provides a comprehensive survey of Deep Reinforcement Learning (DRL)-assisted resource allocation strategies tailored for FAS. It explores the integration of DRL algorithms to leverage FAS's inherent flexibility, addressing scalability and reliability challenges. Our findings highlight the ability of DRL-enhanced resource allocation to improve network capacity and resilience, underscoring the transformative potential of FAS in next-generation wireless systems. Furthermore, we discuss key research challenges and future trends in this evolving domain, setting a roadmap for future advancements.