基于深度强化学习的RIS辅助UAV通信轨迹与相移联合优化研究综述

王恩良; 李琨; 张炜荥; 孙知信

doi:10.12263/DZXB.20260377

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基于深度强化学习的RIS辅助UAV通信轨迹与相移联合优化研究综述

综述评论 | 更新时间：2026-10-16

- 基于深度强化学习的RIS辅助UAV通信轨迹与相移联合优化研究综述
- A Review of Joint Trajectory and Phase-Shift Optimization in RIS-Assisted UAV Communication Based on Deep Reinforcement Learning
- 电子学报 2026年54卷第6期页码：2879-2898
- 作者机构：
  
  1.南京邮电大学江苏省邮政大数据技术与应用工程研究中心，江苏南京 210023
  2.南京邮电大学国家邮政局邮政行业技术研发中心（物联网技术），江苏南京 210023
- 作者简介：
  
  王恩良男，1998年2月出生于江苏省南京市。现为南京邮电大学物联网学院博士研究生。主要研究方向为神经架构原理、强化学习、网络与通信技术。E-mail: 3386204603@qq.com
  李琨男，2001年12月出生于山东省潍坊市。现为南京邮电大学现代邮政学院硕士研究生。主要研究方向为强化学习、低空技术。E-mail: 19563132060@163.com
  张炜荥男，2001年6月出生于福建省福州市。现为南京邮电大学现代邮政学院硕士研究生。主要研究方向为强化学习。E-mail: 1224097333@njupt.edu.cn
  孙知信男，1964年9月出生于江苏省南京市。现为南京邮电大学现代邮政学院教授、博士生导师。主要研究方向为物联网技术、计算机网络、通信技术。E-mail: Sunzx@njupt.edu.cn
- 基金信息：
  
  国家自然科学基金(62272239);贵州省重点技术研发计划(［2023］272)
- DOI：10.12263/DZXB.20260377
  中图分类号： TN929.5;TP18
- 收稿：2026-04-22，
  
  录用：2026-05-27，
  
  纸质出版：2026-06-25
- 稿件说明：
移动端阅览
王恩良, 李琨, 张炜荥, 等. 基于深度强化学习的RIS辅助UAV通信轨迹与相移联合优化研究综述[J]. 电子学报, 2026, 54(06): 2879-2898.

WANG Enliang, LI Kun, ZHANG Weiying, et al. A Review of Joint Trajectory and Phase-Shift Optimization in RIS-Assisted UAV Communication Based on Deep Reinforcement Learning[J]. Acta Electronica Sinica, 2026, 54(06): 2879-2898.
王恩良, 李琨, 张炜荥, 等. 基于深度强化学习的RIS辅助UAV通信轨迹与相移联合优化研究综述[J]. 电子学报, 2026, 54(06): 2879-2898. DOI：10.12263/DZXB.20260377

WANG Enliang, LI Kun, ZHANG Weiying, et al. A Review of Joint Trajectory and Phase-Shift Optimization in RIS-Assisted UAV Communication Based on Deep Reinforcement Learning[J]. Acta Electronica Sinica, 2026, 54(06): 2879-2898. DOI：10.12263/DZXB.20260377

摘要

可重构智能表面（Reconfigurable Intelligent Surface，RIS）与无人机（Unmanned Aerial Vehicle，UAV）的协同部署为提升未来无线网络的覆盖范围、传输容量和服务灵活性提供了新的技术路径，其核心在于UAV轨迹、RIS相移矩阵、主动波束成形及资源分配变量的联合优化。该问题具有跨时隙耦合、动作维度高、约束非凸和环境动态变化等特征，传统交替优化、逐次凸近似和半正定松弛方法在大规模或快速时变场景中面临迭代开销较高、在线适应性不足以及对精确信道状态信息依赖较强等问题。深度强化学习（Deep Reinforcement Learning，DRL）能够将上述联合优化表述为序贯决策过程，为复杂动态环境下的在线策略生成提供新的求解思路。本文围绕基于DRL的RIS辅助UAV通信轨迹—相移联合优化开展系统综述。首先，建立包含轨迹、相移和波束成形变量的统一优化框架，并从动作空间、状态空间和奖励函数三个维度分析马尔可夫决策过程建模的关键选择。随后，依据联合动作的表示与分解方式，将现有研究归纳为离散决策、连续决策和分布式协同三类技术路线，比较其算法机制、适用场景、样本效率、收敛稳定性、可扩展性与工程代价。进一步，从可扩展性、信道状态信息假设、仿真—部署迁移和公平比较基准四个方面提炼该领域的共性瓶颈。综合分析表明，离散方法适用于中小规模RIS和低比特相移控制；连续方法具有较高控制精度，但易受高维策略学习与样本效率限制；多智能体方法与分布式系统架构较为匹配，但会引入环境非平稳性和信用分配问题。在大规模场景下，现有方法普遍依赖结构化降维或DRL与传统优化相结合的混合分解，严格意义上的端到端联合求解仍较少。最后，从结构化DRL、离线强化学习与数字孪生、标准化评测平台、近场与太赫兹通信以及通感算一体化等方面讨论未来研究方向，为相关算法设计、性能评估与系统部署提供参考。

Abstract

The collaborative deployment of reconfigurable intelligent surfaces (RISs) and unmanned aerial vehicles (UAVs) provides a promising technological approach to improving the coverage

transmission capacity

and service flexibility of future wireless networks. Its central challenge lies in the joint optimization of UAV trajectories

RIS phase-shift matrices

active beamforming

and resource allocation variables. This problem is characterized by inter-slot coupling

high-dimensional action spaces

nonconvex constraints

and dynamically varying environments. In large-scale or rapidly time-varying scenarios

conventional methods based on alternating optimization

successive convex approximation

and semidefinite relaxation generally suffer from high iterative complexity

limited online adaptability

and a strong dependence on accurate channel state information. Deep reinforcement learning (DRL) can formulate the joint optimization problem as a sequential decision-making process

thereby providing a new approach to online policy generation in complex dynamic environments. This paper presents a systematic review of DRL-based joint trajectory and phase-shift optimization in RIS-assisted UAV communications. First

a unified optimization framework incorporating trajectory

phase-shift

and beamforming variables is established

and the key choices in Markov decision process (MDP) modeling are analyzed from the perspectives of action-space design

state-space construction

and reward-function formulation. Existing studies are then classified into three technical paradigms according to the representation and decomposition of joint actions: discrete decision-making

continuous decision-making

and distributed cooperation. Their algorithmic mechanisms

applicable scenarios

sample efficiency

convergence stability

scalability

and implementation costs are comparatively examined. Four common bottlenecks are further identified

namely limited scalability

idealized channel state information (CSI) assumptions

the simulation-to-deployment gap

and the lack of fair benchmarking protocols. The comparative analysis shows that discrete methods are suitable for small- and medium-scale RISs with low-bit phase-shift control

whereas continuous methods provide higher control precision but are constrained by high-dimensional policy learning and limited sample efficiency. Multi-agent methods are well matched to distributed system architectures but introduce environmental nonstationarity and credit-assignment difficulties. In large-scale scenarios

most existing approaches rely on structured dimensionality reduction or hybrid decomposition combining DRL with conventional optimization

while strictly end-to-end joint optimization remains relatively uncommon. Finally

future research directions are discussed

including structured DRL architectures

offline reinforcement learning and digital twins

standardized benchmarking platforms

near-field and terahertz communications

and integrated communication

sensing

and computing. This review provides a reference for subsequent algorithm design

performance evaluation

and system deployment.

关键词

Keywords

references

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