基于能量感知的智能反射面辅助无人机时效数据收集策略  

Energy Aware Reconfigurable Intelligent Surface Assisted Unmanned Aerial Vehicle Age of Information Enabled Data Collection Policies

作　　者：张涛 张迁 朱颖雯 代陈 ZHANG Tao;ZHANG Qian;ZHU Yingwen;DAI Chen(School of Information Technology,Jiangsu Open University,Nanjing 210000,China;College of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China;School of Computer Science,Nanjing University of Posts and Telecommunications,Nanjing 210023,China)

机构地区：[1]江苏开放大学信息工程学院,南京210000 [2]南京航空航天大学计算机科学与技术学院,南京210016 [3]南京邮电大学计算机学院,南京210023

出　　处：《电子与信息学报》2025年第2期427-438,共12页Journal of Electronics & Information Technology

基　　金：国家自然科学基金(62402232);江苏省高等学校自然科学研究项目(23KJB520024)。

摘　　要：为了应对智能反射面(RIS)辅助的无人机(UAV)在物联网数据收集过程中能量高效利用与信息收集时效性之间的均衡问题,该文提出一种基于深度强化学习的数据收集优化策略。针对无人机在数据采集过程中的飞行能耗、通信复杂性及采集信息时效性(AoI)约束,设计了一种基于双深度Q网络(DDQN)的联合优化方案,涵盖无人机轨迹规划、物联网设备调度以及智能反射面相位调整。该方案有效缓解了传统Q学习方法中Q值过估计的问题,使无人机能够根据实时环境动态调整飞行轨迹和通信策略,从而在提升数据传输效率的同时降低能量消耗。仿真结果表明,与传统方法相比,所提方案能够显著提高数据收集效率。此外,通过合理分配能量与通信资源,所提方案能够动态适应不同通信环境参数变化,确保系统在能耗与AoI之间达到最佳均衡。Objective This study aims to develop and implement an optimization framework that addresses the critical balance between energy consumption and information freshness in Unmanned Aerial Vehicle(UAV)-assisted Internet of Things(IoT)data collection systems,enhanced by Reconfigurable Intelligent Surfaces(RIS).In complex urban environments,traditional line-of-sight communication between UAVs and ground-based IoT devices is often obstructed by buildings and infrastructure,hindering comprehensive coverage and efficient data collection.While RIS technology offers promising solutions by dynamically adjusting signal reflection directions,optimizing communication signal coverage,and enhancing quality,it introduces additional complexity in system design and resource allocation,requiring sophisticated adaptive optimization techniques.The integration of RIS enables stable communication connections across various UAV flight heights and angles,mitigating disruptions caused by obstacles or signal interference,thus improving data collection efficiency and reliability.However,this integration must account for multiple factors,including UAV energy consumption,communication complexity,and Age of Information(AoI)constraints.These approaches must adapt to the dynamic nature of UAV operations and fluctuating communication conditions,ensuring optimal performance in terms of energy efficiency and data freshness.The research also addresses several key challenges,including real-time adaptation to environmental changes,optimal scheduling of IoT device interactions,dynamic adjustment of RIS phase configurations,efficient trajectory planning,and the maintenance of data freshness under various system constraints.The proposed framework establishes a robust foundation for next-generation IoT data collection systems that can adapt to diverse operational conditions while maintaining high performance standards.This is achieved through the implementation of advanced deep reinforcement learning techniques,specifically designed to manage the complex in

关 键 词：无人机辅助通信 时效性 深度强化学习 智能反射面 

分 类 号：TN929.5[电子电信—通信与信息系统]