SwarmShare: 6ghz频段蜂群无人机网络的移动弹性频谱共享

Jiangqi Hu, Sabarish Krishna Moorthy, Ankush Harindranath, Zhangyu Guan, Nicholas Mastronarde, E. Bentley, Scott M. Pudlewski
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引用次数: 4

摘要

为了缓解长期的频谱紧张问题,美国联邦通信委员会最近开放了6ghz频段供未经许可的使用。然而,现有的频谱共享策略无法支持汽车和无人机等移动车辆中接入点的运行。这主要是由于该频段内在位系统之间基于方向性的频谱共享以及移动车辆的高移动性给控制跨系统干扰带来了挑战。本文提出了一种用于6ghz频段蜂群无人机组网的移动弹性频谱共享框架——swarm share。我们首先提出了一个SwarmShare问题的数学公式,该问题的目标是在现有系统的干扰约束下,通过共同控制无人机的飞行和发射功率以及它们与地面用户的关联,最大化无人机网络的频谱效率。我们发现没有封闭的数学模型可以用来描述无人机对在位系统的总体干扰的统计行为。然后,我们提出了一种数据驱动的三阶段频谱共享方法,包括初始功率强制、离线数据集引导的在线功率自适应和基于强化学习的无人机优化。我们通过广泛的模拟活动验证了SwarmShare的有效性。结果表明,在不需要实时跨系统信道状态信息的情况下,基于swarm的无人机对在位系统的综合干扰可以有效地控制在目标水平以下。在没有精确无人机位置信息的共存网络中,验证了swarm的机动弹性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
SwarmShare: Mobility-Resilient Spectrum Sharing for Swarm UAV Networking in the 6 GHz Band
To mitigate the long-term spectrum crunch problem, the FCC recently opened up the 6 GHz frequency band for unlicensed use. However, the existing spectrum sharing strategies cannot support the operation of access points in moving vehicles such as cars and UAVs. This is primarily because of the directionality-based spectrum sharing among the incumbent systems in this band and the high mobility of the moving vehicles, which together make it challenging to control the cross-system interference. In this paper we propose SwarmShare, a mobility-resilient spectrum sharing framework for swarm UAV networking in the 6 GHz band. We first present a mathematical formulation of the SwarmShare problem, where the objective is to maximize the spectral efficiency of the UAV network by jointly controlling the flight and transmission power of the UAVs and their association with the ground users, under the interference constraints of the incumbent system. We find that there are no closed-form mathematical models that can be used characterize the statistical behaviors of the aggregate interference from the UAVs to the incumbent system. Then we propose a data-driven three-phase spectrum sharing approach, including Initial Power Enforcement, Offline-dataset Guided Online Power Adaptation, and Reinforcement Learning-based UAV Optimization. We validate the effectiveness of SwarmShare through an extensive simulation campaign. Results indicate that, based on SwarmShare, the aggregate interference from the UAVs to the incumbent system can be effectively controlled below the target level without requiring the real-time cross-system channel state information. The mobility resilience of SwarmShare is also validated in coexisting networks with no precise UAV location information.
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