Ishtiaq Ahmad, Ph.D.

Všechny publikace

Machine Learning-Based Beamforming for Unmanned Aerial Vehicles Equipped with Reconfigurable Intelligent Surfaces

  • DOI: 10.1109/MWC.004.2100694
  • Odkaz:
  • Pracoviště: Katedra telekomunikační techniky
  • Anotace:
    Unmanned aerial vehicles (UAVs) equipped with reconfigurable intelligent surfaces (RISs) have emerged as a promising technology for numerous applications involving aerial networks. However, the UAV-RIS concept faces challenges related to the deployment of the UAV-RIS, especially in cases, where UAV-RIS is combined with emerging technologies, such as beamforming, sensitive to propagation channel variation. In this article, we first overview various use-cases of UAV-RIS beam-forming considering practical scenarios. Aiming to improve the performance of communication channels, we propose a machine learning-based beamforming policy for UAV-RIS by employing prioritized experience replay (PER) based deep Q-Network (DQN). Compared to traditional approaches, the proposed PER DQN-based beamforming for UAV-RIS communication provides significant enhancements in performance. Finally, we highlight some potential directions for future research.

Za stránku zodpovídá: Ing. Mgr. Radovan Suk