Fluid antenna systems (FAS) provide flexible spatial diversity through adaptive port activation, but existing FAS port selection methods mainly assume static propagation environments or full instantaneous channel state information (CSI), making them less suitable for dynamic UAV-to-ground (U2G) communications. This letter investigates adaptive FAS port activation for a UAV receiver under dynamic propagation conditions and partial pilot observations. A graph neural network (GNN)-based utility predictor is first proposed to infer the utilities of candidate ports by exploiting spatial correlation among FAS ports. Based on the predicted utilities, a Budgeted-Greedy port selection strategy is then developed to jointly consider utility, spatial diversity, and switching overhead. Simulation results demonstrate that the proposed framework consistently achieves a favorable trade-off between spectral efficiency and port switching. These results indicate the effectiveness of jointly exploiting spatial correlation and switching-aware port selection for the considered dynamic UAV-FAS communications.
Rotatable directional antennas have recently emerged as a low-complexity alternative to conventional digital beamforming architectures for uncrewed aerial vehicle (UAV)-assisted wireless communication systems. In particular, tethered UAVs equipped with rotatable directional antennas can provide adaptive directional tra...
Abdulrahman Al-Hababi, Zain Ali, Rula Ammuri et al.· IEEE Open Journal of Vehicul...· 0 citations
Fluid antenna systems (FAS) obtain spatial degrees of freedom by activating a few radiating ports from a dense grid of candidates, but deciding which ports to activate presupposes channel knowledge at ports that are never measured. Prior port-selection work assumes full or near-full channel state information (CSI) and...
This paper investigates joint beamforming and port selection in multi-cell fluid antenna-assisted (FAS) networks. In such networks, active beamforming and discrete FA port selection are coupled through intra-cell and inter-cell interference and are jointly optimized to maximize the weighted sum-rate (WSR). We develop a...
Li-Wen Gao, Li Zheng, Xing Hao et al.· 0 citations
This work considers a fluid-antenna assisted status update system supported by multiple source nodes monitoring the same environmental status, and proposes an efficient bisection algorithm for optimal update scheduling which efficiently eliminates all non-optimal source scheduling decisions.
Xiao-Peng Yuan, Paul Zheng, A. Schmeink· 0 citations
The results demonstrate that integrating time–space RIS partitioning with constraint- aware deep reinforcement learning can effectively balance communication utility, information timeliness, and sensing reliability in dynamic vehicular environments.
Xue-Mei Bai, Jia-Rui Yuan, Chen-Jie Zhang et al.· Recent Advances in Computer...· 0 citations
This paper proposes a Deep Deterministic Policy Gradient (DDPG)-based adaptive access point (AP) selection framework for cell-free massive multiple-input multiple-output (CF-mMIMO) systems in beyond-5G and 6G networks. Unlike conventional full-cooperation or heuristic clustering methods, the proposed approach learns a...
