基于通感融合的无人机预编码及飞行轨迹设计

柴蓉; 崔相霖; 孙瑞锦; 陈前斌

doi:10.11999/JEIT230515

基于通感融合的无人机预编码及飞行轨迹设计

doi: 10.11999/JEIT230515

1.
重庆邮电大学通信与信息工程学院重庆 400065
2.
综合业务网理论及关键技术国家重点实验室西安电子科技大学西安 710126

基金项目: 国家自然科学基金(62271097)

详细信息

作者简介:
柴蓉：女，教授，研究方向为通信网络架构及关键技术、无线资源管理及移动性管理技术等

崔相霖：男，硕士生，研究方向为无线通信、无线资源管理等

孙瑞锦：女，菁英副教授，研究方向为知识驱动的资源调度

陈前斌：男，教授，研究方向为无线通信、通信网络理论、软件定义网络、多媒体技术等

通讯作者:
柴蓉　chairong@cqupt.edu.cn

中图分类号: TN915
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- 被引次数: 0
出版历程
- 收稿日期: 2023-05-30
- 修回日期: 2023-12-01
- 网络出版日期: 2023-12-06
- 刊出日期: 2024-04-24

Precoding and Trajectory Design of Unmanned Aerial Vehicle Based on Joint Communication and Sensing

1.
School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
State Key Laboratory of Integrated Services Networks, Xidian University, Xi’an 710126, China

Funds: The National Natural Science Foundation of China (62271097)

摘要

摘要: 无人机(UAVs)具有机动性强，低成本及易部署等特性，通过搭载通信及感知设备，支持通信与感知技术的高效资源共享，无人机可作为融合通信与传感技术的高性能空中平台。该文针对多输入多输出(MIMO)无人机使能的联合通信、感知场景，综合考虑无人机飞行能量、多天线传输及用户业务需求等限制条件，建模无人机通信、感知预编码及飞行轨迹联合优化问题为多目标优化问题，以实现通信用户最低速率最大化及目标最小发现概率最大化。由于通信用户最低速率最大化问题为非凸优化问题，难以直接求解，将原优化问题分解为通信预编码设计子问题及无人机轨迹设计子问题，并采用交替迭代法依次求解两个子问题直至算法收敛，其中，对于通信预编码设计子问题，提出一种基于迫零(ZF)算法的求解策略；对于无人机轨迹设计子问题，提出一种基于连续凸逼近(SCA)算法的求解策略。基于所得到的无人机最优轨迹，将无人机感知位置选择问题建模为加权距离和最小化问题，进而应用泛搜索算法优化确定目标感知位置，并设计基于ZF算法的通信感知预编码联合优化策略，以实现通信感知性能的联合优化。最后通过仿真验证了该文所提算法的有效性。
- 无人机 /
- 通感联合 /
- 轨迹优化 /
- 预编码设计
Abstract: Benefited from the characteristics of high mobility, low cost and convenient deployment, by deploying communication and sensing equipment and supporting efficient resource sharing of communication and sensing technologies, Unmanned Aerial Vehicles (UAVs) are expected to act as high-performance aerial platforms which integrate communication and sensing technologies. In this paper, Multiple-Input-Multiple-Output (MIMO) UAV-enabled joint communication and sensing scenario is examined, the constraints of the flight energy of the UAV, multi-antenna transmission and user service requirement are jointly considered, the problem of UAV communication, sensing precoding and flight trajectory is formulated as a multi-objective optimization problem which jointly maximizes the minimum data rate of communication users and the minimum discovery probability of targets. Since the minimum rate maximization problem of communication users is a non-convex optimization problem, which is difficult to solve directly, the original optimization problem is decomposed into communication precoding design subproblem and UAV trajectory design subproblem. The two subproblems are solved successively by applying alternate iteration method until the algorithm reaches convergence. Specifically, a Zero-Forcing (ZF) algorithm is put forward for solving the communication precoding design subproblem. A Successive Convex Approximation (SCA) algorithm is applied to determine the optimal trajectory of the UAV. Based on the optimal trajectory of UAV, the sensing location selection problem is modeled as a weighted distance minimization problem, and then the extensive search algorithm is applied to obtain the optimal locations. Finally, a ZF algorithm-based joint communication and sensing precoding is presented. The effectiveness of the proposed algorithm is verified by simulations.
- Unmanned Aerial Vehicle (UAV) /
- Joint communication and sensing /
- Trajectory optimization /
- Precoder design

HTML全文

图 1 系统模型图

下载: 全尺寸图片幻灯片

图 2 无人机飞行轨迹

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图 3 无人机飞行轨迹及目标感知位置图

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图 4 用户最低通信速率对比

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图 5 算法对比

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图 6 感知性能对比

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表 1 仿真参数设置

仿真参数	数值
LOS特性系数(ρ₀)	10^–2
飞行高度(L)	50 m
噪声标准差(σ²)	10^–10 W
无人机速度上限(V_max)	30 m/s
无人机飞行能量系数(k₁,k₂)	0.066 1, 15.97
飞行通信能量上限(E_c)	100 J
无人机飞行能量上限	4×10⁴ J, 8×10⁴ J
无人机通感联合能量上限(E_s)	100 J
每时隙通信发射功率上限($ p_{\max }^{\text{c}} $)	1.5 W
每时隙感知发射功率上限($ p_{\max }^{\text{s}} $)	30 W
雷达最小接收灵敏度(S_{i min})	–90 dBm

下载: 导出CSV

参考文献(16)

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