基于边缘计算的无人机通感融合网络波束成形与资源优化

李斌; 彭思聪; 费泽松

doi:10.11959/j.issn.1000-436x.2023172

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基于边缘计算的无人机通感融合网络波束成形与资源优化

学术通信 | 更新时间：2024-06-06

- 基于边缘计算的无人机通感融合网络波束成形与资源优化
- Beamforming and resource optimization in UAV integrated sensing and communication network with edge computing
- 通信学报 2023年44卷第9期页码：228-237
- 作者机构：
  
  1. 南京信息工程大学计算机学院，江苏南京 210044
  2. 北京理工大学信息与电子学院，北京 100081
- 作者简介：
  
  [ "李斌（1987- ），男，山东济宁人，博士，南京信息工程大学副教授、硕士生导师，主要研究方向为无人机通信、移动边缘计算等" ]
  [ "彭思聪（2000- ），男，江苏盐城人，南京信息工程大学硕士生，主要研究方向为移动边缘计算、通感算一体化等" ]
  [ "费泽松（1977- ），男，安徽合肥人，博士，北京理工大学教授、博士生导师，主要研究方向为无线通信、多媒体信号处理等" ]
- 基金信息：
  
  国家重点研发计划基金资助项目(2021YFB2900200);国家自然科学基金资助项目(62101277);江苏省自然科学基金资助项目(BK20200822)
- DOI：10.11959/j.issn.1000-436x.2023172
  中图分类号： TN92
- 网络首发：2023-09，
  
  纸质出版：2023-09-25
- 稿件说明：
移动端阅览
李斌, 彭思聪, 费泽松. 基于边缘计算的无人机通感融合网络波束成形与资源优化[J]. 通信学报, 2023,44(9):228-237.

Bin LI, Sicong PENG, Zesong FEI. Beamforming and resource optimization in UAV integrated sensing and communication network with edge computing[J]. Journal on Communications, 2023, 44(9): 228-237.
李斌, 彭思聪, 费泽松. 基于边缘计算的无人机通感融合网络波束成形与资源优化[J]. 通信学报, 2023,44(9):228-237. DOI： 10.11959/j.issn.1000-436x.2023172.

Bin LI, Sicong PENG, Zesong FEI. Beamforming and resource optimization in UAV integrated sensing and communication network with edge computing[J]. Journal on Communications, 2023, 44(9): 228-237. DOI： 10.11959/j.issn.1000-436x.2023172.

摘要

为了解决传统通信-感知融合网络模式对地面基础设施的依赖，针对复杂场景下通感融合网络系统功耗较大、信号阻塞、覆盖盲区等问题，提出了一种无人机搭载边缘计算服务器与雷达收发器辅助通感融合网络。首先，在满足用户传输功率、雷达估计信息率、任务卸载比例限制的条件下，通过联合优化无人机雷达波束成形、计算资源分配问题、任务卸载量划分、终端用户发射功率和无人机飞行轨迹，建立系统总能耗最小化问题；其次，将该非凸优化问题重新构建为一个马尔可夫决策过程，使用深度强化学习中的近端策略优化算法实现系统的优化决策。仿真结果表明，所提算法训练速度较快，能够在保证应用的感知与计算时延需求的同时有效降低系统能耗。

Abstract

To address the dependence of traditional integrated sensing and communication network mode on ground infrastructure

the unmanned aerial vehicle (UAV) with edge computing server and radar transceiver was proposed to solve the problems of high-power consumption

signal blocking

and coverage blind spots in complex scenarios.Firstly

under the conditions of satisfying the user’s transmission power

radar estimation information rate and task offloading proportion limit

the system energy consumption was minimized by jointly optimizing UAV radar beamforming

computing resource allocation

task offloading

user transmission power

and UAV flight trajectory.Secondly

the non-convex optimization problem was reformulated as a Markov decision process

and the proximal policy optimization method based deep reinforcement learning was used to achieve the optimal solution.Simulation results show that the proposed algorithm has a faster training speed and can reduce the system energy consumption effectively while satisfying the sensing and computing delay requirements.

关键词

Keywords

references

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