User centric cooperative transmission mechanism in UAV-based multi-RIS networks

Feng Yanru; Yan Junjie; Liu Hanyong

doi:10.11959/j.issn.1000-0801.2026100

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User centric cooperative transmission mechanism in UAV-based multi-RIS networks

Topic: low‑altitude communication | 更新时间：2026-03-31

- User centric cooperative transmission mechanism in UAV-based multi-RIS networks
- Telecommunications Science Vol. 42, Pages: 1-14(2026)
- 作者机构：
  
  1.山西应用科技学院，山西太原 030062
  2.广西科技大学电子工程学院，广西柳州 545006
  3.国家电网重庆市电力公司大足供电分公司，重庆 402360
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(62501178);Science and Technology Innovation Projects of Colleges and Universities in Shanxi Province(2023L502)
- DOI：10.11959/j.issn.1000-0801.2026100
  CLC： TN929.5
- Revised：2025-12-09，
  
  Accepted：25 December 2025，
  
  Online First：30 March 2026，
  
  Published：20 April 2026
- 稿件说明：
移动端阅览
冯艳如,闫俊杰,刘汉永.用户为中心的多RIS辅助无人机协作传输机制[J].电信科学,

Feng Yanru,Yan Junjie,Liu Hanyong.User centric cooperative transmission mechanism in UAV-based multi-RIS networks[J].Telecommunications Science,
冯艳如,闫俊杰,刘汉永.用户为中心的多RIS辅助无人机协作传输机制[J].电信科学, DOI：10.11959/j.issn.1000−0801.2026100.

Feng Yanru,Yan Junjie,Liu Hanyong.User centric cooperative transmission mechanism in UAV-based multi-RIS networks[J].Telecommunications Science, DOI：10.11959/j.issn.1000−0801.2026100.

摘要

针对多可重构智能超表面（reconfigurable intelligent surface，RIS）辅助的无人机（unmanned aerial vehicle，UAV）通信网络中用户链路需求与动态资源管理所带来的挑战，提出了一种基于深度强化学习的用户中心化协作传输机制。构建MINLP模型，联合优化UAV飞行轨迹、传输功率、RIS反射单元分配及相移策略，在满足用户差异化服务质量需求的同时，最大化系统能效。为解决传统方法复杂度高、动态环境适应性不足的问题，设计了一种多阶段优化框架：第一阶段采用联盟博弈算法实现RIS反射单元的动态分配，并引入用户自主切换机制以降低同频干扰；第二阶段利用双延迟深度确定性策略梯度（twin delayed deep deterministic policy gradient，TD3）算法，联合优化资源分配与UAV轨迹。仿真结果表明，所提方案在系统能效方面均优于对比基准方案，且相较于SAC基线算法，系统能效提升19%，有效验证了该方案在动态环境下的鲁棒性与对用户需求差异化服务质量的保障能力。

Abstract

To address the challenges of differentiated user link demands and dynamic resource management in unmanned aerial vehicle (UAV) communication networks assisted by multiple reconfigurable intelligent surfaces (RIS)

a user-centric cooperative transmission mechanism based on deep reinforcement learning was proposed. A mixed integer nonlinear programming (MINLP) model was formulated to jointly optimize UAV flight trajectory

transmissiont power

RIS reflection unit allocation

and phase shift control

aiming to maximize system energy efficiency while satisfying differentiated quality of service (QoS) requirements. To address the issues of high computational complexity and the limited adaptability to dynamic environments

a multi-stage optimization framework was designed. In the first stage

a coalition game-based algorithm was introduced to dynamically allocate RIS elements

and a user autonomous switching mechanism was introduced to mitigate co-channel interference. In the second stage

the twin delayed deep deterministic policy gradient (TD3) algorithm was adopted to jointly optimize resource allocation and UAV trajectory planning. Simulation results demonstrated that the proposed scheme consistently outperforms all benchmark methods in terms of system energy efficiency

achieving a 19% improvement compared to SAC algorithms

thereby effectively verifying its robustness in dynamic environments and its capability to guarantee differentiated QoS for users.

关键词

Keywords

references

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