无人机机间毫米波阵列通信技术

doi:10.11959/j.issn.1000-436x.2022158

Abstract

Abstract:

As the application scope of unmanned aerial vehicle (UAV) expands and the tasks become more complicated, the demands for large bandwidth, high data rate and antijamming capability in UAV-to-UAV (V2V) communication grow rapidly.With abundant spectrum resource, millimeter-wave (mmWave) array communication has been one of the key technologies to support V2V communications.Focusing on the emerging field of mmWave array V2V communication, the difficulties and challenges in point-to-point communication and unmanned aerial vehicle ad-hoc network were analyzed.The problems and approaches for channel modeling, robust beamforming, secure communication in point-to-point communication and neighbor discovery, routing decisions, resource allocation, distributed deployment in unmanned aerial vehicle ad-hoc network were reviewed.Some valuable research directions were summarized to provide inspiration for future relevant research.

Key words: UAV communication, mmWave communication, array antenna, point-to-point communication, unmanned aerial vehicle ad-hoc network

CLC Number:

TN928

Zhenyu XIAO, Ke LIU, Lipeng ZHU. Millimeter-wave array enabled UAV-to-UAV communication technology[J]. Journal on Communications, 2022, 43(10): 196-209.

Figures/Tables 8

References 85

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机间通信类型	应用需求	主要挑战	毫米波阵列的技术支持
无人机点对点通信	信道建模	收发无人机位置与姿态快速变化，信道指向角快速变化，多普勒效应严重；现有的空对地通信信道模型并不适用于无人机机间通信	毫米波信号在角度域离散、动态波束追踪
	稳健性波束成形	高指向性毫米波对于角度偏差敏感，需要快速波束追踪实时获取信道状态信息，无人机高速运动平台在毫米波频段多普勒频移严重	低复杂度稳健性波束成形、自适应波束追踪、局部空间高增益波束
	安全通信	无线传输具有开放性和广播性，容易泄露信息	为跳频、扩频技术提供更多选择、三维波束成形为安全通信提供空域自由度
无人机自组网	邻居发现	三维全空域邻居发现；频繁波束追踪；广播信号传播范围有限且隐蔽性较差	高指向性波束、任意波束宽度灵活波束成形、动态波束追踪
	路由决策	节点移动速度快、网络拓扑结构变化快；高数据流量需求；低时延需求；与资源分配相耦合	指向性波束增加传输距离、减少路由建立时间、减少数据包碰撞
	资源分配	对空域、时域、频域、功率资源的高效分配；资源冲突和资源竞争问题；与分布式部署相耦合	毫米波波段频谱资源分配、空间复用与波束追踪、灵活功率分配
	分布式部署	与资源分配相耦合，多变量非凸问题难以求解；毫米波覆盖范围有限；波束调度任务复杂	动态全空域波束追踪、波束移交

文献	天线类型	多径类型	位置姿态变化	多普勒频移
文献[18]	UPA	LoS	位置和姿态	—
文献[19]	UPA	LoS+NLoS	位置	—
文献[20]	ULA	LoS+NLoS	位置	Y
文献[21]	全向天线	LoS+NLoS	位置	Y
文献[22]	UPA	LoS+NLoS	位置	Y

研究内容	文献	主要贡献	优缺点
波束追踪	文献[18,28]	利用高斯过程机器学习算法预测无人机的位置和姿态，	优点：预测准确度高、频谱效率高，适用于高动态无人
		估计信号的AoA和AoD，进而进行波束追踪	机自组网
			缺点：没有讨论时间效率
	文献[29]	提出了基于Q学习的波束追踪算法	优点：可以在多无人机高动态环境中实现平稳追踪
			缺点：可能需要极大的训练开销
	文献[30]	基于机器学习的预测波束成形方案解决由无人机抖动引	优点：无人机和终端提前准备波束并快速建立和维持通
		起的波束失准	信链路
			缺点：抖动只存在于发射端无人机
多普勒	文献[31]	根据历史估计结果优化估计过程，实现准确、低复杂度	优点：能够实现快速准确的多普勒频移补偿
频移补偿		的多普勒频移估计，进而进行有效的多普勒频移补偿	缺点：信道模型采用统计模型

协议类型	文献	工作模式	特点
基于拓扑路由	文献[61-62]	根据整个网络的拓扑信息在数据包发送前确定从源节点到目的节点的传输路径	路由发现数据包在整个网络中散播，路由表定期更新，对带宽、可扩展性、存储功能要求较高，开销大
基于位置路由	文献[63-64]	利用每个节点已知的自身位置信息和当前邻居节点的位置信息确定传输路径	不需要全局网络信息，只需要源节点、邻居节点和目的节点的位置信息，能耗低、开销小，计算时间短，可扩展性强
混合路由	文献[65-66]	当数据无法通过基于拓扑的路由到达目的节点时，采用基于位置路由将数据转发给离目的节点最近的邻居节点，直至数据到达目的节点	能够在快速移动场景下实现更高的数据包传送率，适用于无人机自组网
仿生路由	文献[67-70]	模仿自然界生物群体的规律和动态，将路由发现过程模拟为蜂群采蜜或蚁群觅食等过程	适应性和自组织能力强，能有效应对网络拓扑结构的快速变化并调整路由，适用于大型网络

Millimeter-wave array enabled UAV-to-UAV communication technology

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