A fluid antenna system (FAS) can improve both energy harvesting and data transmission in wireless powered communication (WPC) by exploiting spatial diversity with a single RF chain. In this letter, we study the outage probability of a FAS-assisted WPC network under the port selection criterion that maximizes the product of the channel gains of the two links. Adopting the block-correlation (BC) decomposition fitted to the Jakes’ autocorrelation function, we derive a semi-analytical outage expression via nested Gauss–Chebyshev quadrature with singularity-removing variable substitutions. Numerical results under the exact Jakes’ correlation confirm the analytical predictions and show that exploiting both links in port selection yields much lower outage probabilities than single-link selection. Moreover, the advantage becomes more pronounced as the aperture increases, and for sufficiently large apertures it further increases with the number of ports. For the considered settings, significant diversity gains from increasing the number of ports are observed at $W=4\lambda $ .
Zhen-Guan Wu, Xia-Zhi Lai, Jun-Teng Yao et al.· IEEE Wireless Communications...· 0 citations
Dual-tier unmanned aerial vehicle (UAV) networks have emerged as a promising architecture for enabling flexible and on-demand services in low-altitude wireless environments. However, the high mobility of UAVs and the dynamic nature of wireless channels introduce significant challenges for beam selection, particularly in achieving efficient and stable medium access under time-varying network conditions. Conventional centralized or static approaches are often inadequate due to their limited adaptability and high signaling overhead. To address these challenges, this paper proposes a dynamic beam selection framework based on evolutionary game learning for dual-tier UAV networks. Specifically, the beam selection process is modeled as an evolutionary game, where multiple UAVs compete for limited beam resources and iteratively adjust their strategies based on local utility. This distributed mechanism enables adaptive and scalable decision-making without requiring global coordination, making it well suited for highly dynamic environments. To further enhance convergence efficiency and robustness, a learning-based optimization method based on Proximal Policy Optimization (PPO) is incorporated to guide strategy evolution. Simulation results demonstrate that the proposed approach achieves superior performance in terms of delay reduction, energy efficiency, and convergence speed compared with baseline schemes. These results validate the effectiveness of evolutionary game learning for dynamic beam selection and highlight its potential for enabling efficient medium access in dual-tier UAV networks.
