概率视距信道下无人机数据收集的混合离线-在线设计
摘要
本文考虑城市区域中的无人机(unmanned aerial vehicle, UAV)赋能无线传感器网络(wireless sensor network, WSN),其中部署一架UAV在给定时长内从分布式传感器节点(sensor nodes, SNs)收集数据。为刻画UAV与SNs间偶发的建筑遮挡,我们采用联合仿真与数据回归方法构建了曼哈顿型城市的概率视距(line-of-sight, LoS)信道模型,其表现为UAV–SN仰角的广义logistic函数形式。我们假设仅先验已知SNs位置与概率LoS信道模型,而UAV可沿飞行实时获取与SNs的瞬时LoS/非LoS信道状态信息(channel state information, CSI)。我们的目标是最大化UAV从所有SNs收集的最小(平均)数据速率。为此,我们通过联合优化UAV三维(3D)轨迹与SNs传输调度,建立一个新的速率最大化问题。尽管因缺乏完整UAV–SNs CSI而难以求得最优解,本文提出一种新颖通用的设计方法,称为混合离线-在线优化,利用统计与实时CSI获得其次优解。本质上,所提方法将UAV轨迹与通信调度的联合设计解耦为两阶段:即基于概率LoS信道模型在飞行前确定UAV路径的离线阶段,以及基于瞬时UAV–SNs CSI与SNs累计个体接收数据量沿离线优化路径自适应调整UAV飞行速度及通信调度的在线阶段。
引用
@article{arxiv.1907.06181,
title = {Hybrid Offline-Online Design for UAV-Enabled Data Harvesting in Probabilistic LoS Channel},
author = {Changsheng You and Rui Zhang},
journal= {arXiv preprint arXiv:1907.06181},
year = {2020}
}
备注
The paper has been submitted to IEEE for possible publication. This is the second of the authors' series works on developing UAV trajectory under the more practical UAV-ground channel model instead of the simplified LoS channel model. The first one is "3D Trajectory Optimization in Rician Fading for UAV-Enabled Data Harvesting"