FlyBeam: Echo State Learning for Joint Flight and Beamforming Control in Wireless UAV Networks
FlyBeam: Echo State Learning for Joint Flight and Beamforming Control in Wireless UAV Networks
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FlyBeam：无线无人机网络中联合飞行和波束成形控制的回波状态学习
DOI：
10.1109/icc42927.2021.9500519
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
E. Bentley
中科院分区：
文献类型：
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作者：
Sabarish Krishna Moorthy;Zhangyu Guan;Scott M. Pudlewski;E. Bentley
本文旨在设计具有分布式波束形成能力的高数据速率群无人机网络。主要挑战是群UAV网络中的波束形成增益受到UAV的飞行高度、它们的运动和由此产生的间歇性链路阻塞以及单个UAV处的信道状态信息（CSI）的可用性的高度影响。为了应对这一挑战，我们提出了FlyBeam，一个基于学习的框架，用于群无人机网络中的联合飞行和波束形成控制。首先，我们提出了一个数学公式的控制问题，其目标是通过联合控制无人机的飞行和分布式波束形成来最大化群无人机网络的吞吐量。然后，设计了一种基于回声状态网络学习和在线强化学习相结合的分布式求解算法。前者是采用在线测量的基础上，通过共同考虑未知的阻塞动态和其他因素，影响波束形成增益的单个无人机近似的效用函数。后者用于指导FlyBeam的开发和探索。FlyBeam的有效性通过广泛的模拟活动进行评估。结果表明，显着（高达450%）的波束形成增益可以实现的FlyBeam。我们还研究了阻塞和无人机飞行高度对波束形成增益的影响。结果发现，这是有点令人惊讶的，更高（而不是更低）的波束形成增益可以实现由FlyBeam密集的堵塞群无人机网络。
This paper aims at designing high-data-rate swarm UAV networks with distributed beamforming capabilities. The primary challenge is that the beamforming gain in swarm UAV networks is highly affected by the UAVs’ flight altitude, their movements and the resulting intermittent link blockages, as well as the availability of channel state information (CSI) at individual UAVs. To address this challenge, we propose FlyBeam, a learning- based framework for joint flight and beamforming control in swarm UAV networks. We first present a mathematical formulation of the control problem with the objective of maximizing the throughput of swarm UAV networks by jointly controlling the flight and distributed beamforming of UAVs. Then, a distributed solution algorithm is designed based on a combination of Echo State Network learning and online reinforcement learning. The former is adopted to approximate the utility function for individual UAVs based on online measurements, by jointly considering the unknown blockage dynamics and other factors that affect the beamforming gain. The latter is used to guide the exploitation and exploration in FlyBeam. The effectiveness of FlyBeam is evaluated through an extensive simulation campaign. Results indicate that significant (up to 450%) beamforming gain can be achieved by FlyBeam. We also investigate the effects of blockages and UAV flight altitude on the beamforming gain. It is found that, which is somewhat surprising, higher (rather than lower) beamforming gain can be achieved by FlyBeam with denser blockages in swarm UAV networks.