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<p>This paper presents an innovative autonomous multi-access edge computing (MEC) framework that integrates multi-reflecting intelligent surfaces (RIS) and unmanned aerial vehicles (UAVs) to enhance the communication and service delivery to user devices (UDs) in next generation networks. The primary objective is to optimize system performance by jointly designing UAV trajectories and RIS-based passive beamforming, minimizing overall system average fitness cost while ensuring energy efficiency. This problem poses significant challenges due to its NP-hard nature and the presence of mixed decision variables, making conventional methods ineffective. To overcome these complexities, we propose a Joint Trajectory Designing and Passive Beamforming Algorithm (JTDPBA) composed of four strategic phases. First, a genetic algorithm with a dynamic population size is employed to update UAV hovering point deployments. Second, the phase shifts of the RIS are optimized to boost data rates between UAVs and UDs. Third, a clustering algorithm capable of determining the optimal number of clusters is applied to group hovering points. Finally, UAV trajectories are constructed using Dijkstra’s algorithm to ensure optimality. Simulation results demonstrate that JTDPBA consistently outperforms compared algorithms by achieving lower average fitness cost and significantly reducing computational time.</p>

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Intelligent trajectory planning and beamforming for sustainable multi-UAV and RIS-assisted MEC systems in next-generation communications

  • 期刊论文

  • 发表日期：

    2025年11月5日

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  • Muhammad Asim,
  • Ahmed A. Abd El-Latif,
  • Mohammed El-Affendi,
  • Samia Allaoua Chelloug

摘要

This paper presents an innovative autonomous multi-access edge computing (MEC) framework that integrates multi-reflecting intelligent surfaces (RIS) and unmanned aerial vehicles (UAVs) to enhance the communication and service delivery to user devices (UDs) in next generation networks. The primary objective is to optimize system performance by jointly designing UAV trajectories and RIS-based passive beamforming, minimizing overall system average fitness cost while ensuring energy efficiency. This problem poses significant challenges due to its NP-hard nature and the presence of mixed decision variables, making conventional methods ineffective. To overcome these complexities, we propose a Joint Trajectory Designing and Passive Beamforming Algorithm (JTDPBA) composed of four strategic phases. First, a genetic algorithm with a dynamic population size is employed to update UAV hovering point deployments. Second, the phase shifts of the RIS are optimized to boost data rates between UAVs and UDs. Third, a clustering algorithm capable of determining the optimal number of clusters is applied to group hovering points. Finally, UAV trajectories are constructed using Dijkstra’s algorithm to ensure optimality. Simulation results demonstrate that JTDPBA consistently outperforms compared algorithms by achieving lower average fitness cost and significantly reducing computational time.