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3D Deployment and Energy Efficiency Optimization Based on DRL for RIS-assisted Air-to-Ground Communications Networks

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY(2024)

Key Laboratory of Information and Communication Systems | Key Laboratory of Universal Wireless Communications | Trobe Univ

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Abstract
Research indicates that replacing relays with the reconfigurable intelligent surface (RIS) can effectively reduce energy consumption under certain conditions. Therefore, introducing RIS into unmanned aerial vehicle (UAV) assisted airto- ground communication networks can further enhance communication performance. In this paper, we propose a RISUAV- assisted communication network scenario that utilizes the zero-forcing (ZF) precoding method to eliminate multi-user interference, and then optimize the BS transmit power by using the Dinkelbach algorithm. To address the optimization problem of bandwidth allocation, RIS phase shifts, and threedimensional (3D) coordinates of the RIS-UAV, we propose two deep reinforcement learning (DRL) algorithms, which are termed D3QN-MM and D3QN-Pure, respectively. Both D3QN-MM and D3QN-Pure utilize the dueling double deep Q-network (D3QN) for optimizing bandwidth allocation and the 3D coordinates of the RIS-UAV. However, D3QN-MM employs the traditional majorizeminimization (MM) algorithm for RIS phase shifts optimization, while D3QN-Pure utilizes the D3QN. By comparing them with other algorithms, such as the DRL algorithm, the advantages of the algorithms proposed in this paper are highlighted. Furthermore, compared to the amplify-and-forward (AF) relay, the RIS can achieve a 48% energy efficiency improvement. Besides, the D3QN-Pure algorithm provided up to 14.3% energy effciency improvement compared to the D3QN-MM algorithm.
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Key words
Autonomous aerial vehicles,Optimization,Trajectory,Three-dimensional displays,Resource management,Relays,Array signal processing,Unmanned aerial vehicle (UAV),reconfigurable intelligent surface (RIS),deep reinforcement learning (DRL),dueling double deep Q-learning (D3QN),three-dimensional (3D) deployment
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要点】:本文提出了一种基于RIS辅助的无人机(UAV)地面通信网络场景,通过零forcing(ZF)预编码消除多用户干扰,并使用Dinkelbach算法优化基站(BS)发射功率,同时提出两种深度强化学习(DRL)算法D3QN-MM和D3QN-Pure解决带宽分配、RIS相位移和RIS-UAV三维坐标优化问题,并通过与其他算法比较突显其优势。

方法】:提出的方法包括使用零forcing(ZF)预编码消除多用户干扰,采用Dinkelbach算法优化BS发射功率,以及两种DRL算法D3QN-MM和D3QN-Pure进行带宽分配和RIS-UAV三维坐标优化。

实验】:实验在RIS辅助的无人机地面通信网络场景中进行,使用零forcing(ZF)预编码和Dinkelbach算法,通过与放大-转发(AF)中继相比,RIS可实现48%的能效提升,D3QN-Pure算法相较于D3QN-MM算法最多提供14.3%的能效提升。