UAV Swarm Position Optimization for High Capacity MIMO Backhaul
UAV Swarm Position Optimization for High Capacity MIMO Backhaul
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DOI：
10.1109/jsac.2021.3088677
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发表时间：
2021-10-01
影响因子：
16.4
通讯作者：
Cabric, Danijela
中科院分区：
文献类型：
--
作者：
Hanna, Samer;Krijestorac, Enes;Cabric, Danijela
与远距离多天线地面站通信的一群合作的无人机可以利用MIMO空间多路复用来扩展容量。由于蜂群和地面站之间的视距传播，MIMO信道高度相关，导致有限的多路复用增益。在本文中，我们对无人机的位置进行了优化，以获得单用户界限所给出的最大MIMO容量。首先推导出达到容量界限的无人机布局的无限集合。在给定初始群位置的情况下，我们建立了最小化无人机到达容量最大化位置集合内的位置的距离的问题。在无人机初始位置已知的情况下，提出了一种基于块坐标下降的离线集中式解决方案。我们还提出了一种在线分布式算法，其中无人机迭代地调整自己的位置以最大化容量。我们提出的方法被证明以从最初的无人机放置位置进行有界平移为代价来显著增加容量。当使用大规模MIMO地面站时，这种容量增加持续存在。通过数值仿真，我们证明了我们的方法在莱斯信道下在无人机运动干扰下的鲁棒性。
A swarm of cooperating UAVs communicating with a distant multiantenna ground station can leverage MIMO spatial multiplexing to scale the capacity. Due to the line-of-sight propagation between the swarm and the ground station, the MIMO channel is highly correlated, leading to limited multiplexing gains. In this paper, we optimize the UAV positions to attain the maximum MIMO capacity given by the single user bound. An infinite set of UAV placements that attains the capacity bound is first derived. Given an initial swarm placement, we formulate the problem of minimizing the distance traveled by the UAVs to reach a placement within the capacity maximizing set of positions. An offline centralized solution to the problem using block coordinate descent is developed assuming known initial positions of UAVs. We also propose an online distributed algorithm, where the UAVs iteratively adjust their positions to maximize the capacity. Our proposed approaches are shown to significantly increase the capacity at the expense of a bounded translation from the initial UAV placements. This capacity increase persists when using a massive MIMO ground station. Using numerical simulations, we show the robustness of our approaches in a Rician channel under UAV motion disturbances.