6G低空通信场景下无人机部署优化综述

doi:10.7527/S1000-6893.2024.31296

Abstract

Abstract:

With the rapid evolution of 6G standards， the application of Unmanned Aerial Vehicles （UAVs） in low-altitude communication networks has become a research hotspot. This paper gives a review of UAV deployment optimization in low-altitude communication scenarios， conducting analysis of both static and dynamic deployment strategies for UAVs， as well as related deployment optimization algorithms. The article first explains three basic strategies for UAV deployment： static deployment， single-UAV dynamic deployment， and multi-UAV dynamic deployment， exploring their advantages in different application scenarios. Then， the UAV deployment optimization models are discussed in terms of the channel model， constraints， and objective functions within low-altitude communication networks. Based on this， the paper systematically compares existing UAV deployment optimization algorithms， analyzing the strengths and weaknesses of each algorithm from multiple perspectives. Finally， future research directions of UAV deployment optimization are forecasted， and emerging technologies such as intelligent reflecting surfaces and integrated communication and sensing are analyzed to provide reference and insights for researchers in the field.

Key words: low-altitude communication network, 6G, Unmanned Aerial Vehicle (UAV), development optimization, resource allocation

CLC Number:

V279

Haijun ZHANG, Qingyue XIA, Xu MA, Chao REN, Yang LU. A review of unmanned aerial vehicles deployment optimization in 6G low-altitude communication scenarios[J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(11): 531296.

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部署类型	3D/2D	优化目标	其他优化变量
无人机静态部署	2D	最大化系统容量	无人机数量，发射功率
	2D	最大化系统容量	信道分配，接入分配
	3D	最大化系统容量	无人机数量，发射功率，信道分配
	2D	最大化用户服务数量	时间、发射功率
	3D	最小化无人机数量
单无人机动态部署	2D	最小化任务完成时间	无人机悬停时间，接入分配
	2D	最小化任务完成时间	分簇策略，无人机速度
	2D	最小化能耗	发射功率
	3D	最大化能效
	2D	最小化AoI	充电站部署位置
多无人机动态部署	3D	最小化能耗与时延	任务分配，发射功率
	2D	最小化能耗与时延	信道接入，接入分配，时隙数量
	2D	最小化任务完成时间

算法类型	适用的无人机部署场景	优点	缺点	文献
智能搜索算法	适用于中等规模问题	可找到全局最优解或近似解，适用于多类应用场景	对于大规模问题计算成本高，易陷入局部最优	［71-74，77］
凸优化	适用于中等规模问题	可处理具有凸约束的优化问题，可以得到全局最优解	对于非凸问题不适用，大规模问题计算成本高	［86-89］
深度强化学习	适合大规模和复杂环境问题，如无人机自主部署	可处理高维状态空间和连续动作空间，具有较好的泛化能力	需要大量数据进行训练，对环境的动态变化敏感	［93，95-97］
多智能体深度强化学习	适用于多无人机协同任务和动态部署环境	在分布式决策和协同优化上具有优势，适合动态环境下的无人机协作部署	策略复杂，易受环境不稳定性影响，训练开销大	［102-105］
图论	适合离散和组合优化问题	算法成熟，在特定环境下求解迅速	对于复杂场景，其轨迹难以转化为图	［24，106-107］
博弈论	取决于无人机博弈的复杂性和参与者数量	有利于解决多无人机协作问题，确定纳什均衡策略解	对于非零和博弈或不完全信息博弈可能难以找到解	［109-111］

A review of unmanned aerial vehicles deployment optimization in 6G low-altitude communication scenarios

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