A Mobility-Resilient Spectrum Sharing Framework for Operating Wireless UAVs in the 6 GHz Band
A Mobility-Resilient Spectrum Sharing Framework for Operating Wireless UAVs in the 6 GHz Band
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DOI：
10.1109/tnet.2023.3274354
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发表时间：
2023-12
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影响因子：
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通讯作者：
Jiangqi Hu;Sabarish Krishna Moorthy;Ankush Harindranath;Zhaoxi Zhang;Zhiyuan Zhao;Nicholas Mastronarde;E. Bentley;Scott M. Pudlewski;Zhangyu Guan
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文献类型：
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作者：
Jiangqi Hu;Sabarish Krishna Moorthy;Ankush Harindranath;Zhaoxi Zhang;Zhiyuan Zhao;Nicholas Mastronarde;E. Bentley;Scott M. Pudlewski;Zhangyu Guan
为了缓解长期频谱紧缩问题，FCC最近开放了6 GHz频段供未经许可的使用。然而，现有的频谱共享策略不能支持诸如汽车和UAV的移动车辆中的接入点的操作。这主要是因为该频带中的现有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，这两者一起使得控制跨系统干扰具有挑战性。在本文中，我们提出了SwarmShare，移动弹性频谱共享框架群无人机网络在6 GHz频段。首先，我们提出了一个数学公式的SwarmShare问题，其目标是通过联合控制的飞行和发射功率的无人机和他们的关联与地面用户，现任系统的干扰约束下，最大限度地提高频谱效率的无人机网络。我们发现，有没有封闭形式的数学模型，可用于表征的统计行为的总干扰无人机的现任系统。然后，我们提出了一种数据驱动的三阶段频谱共享方法，包括初始功率执行，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。仿真结果表明，基于SwarmShare算法，在不需要实时获取跨系统信道状态信息的情况下，可以有效地将无人机对系统的干扰抑制在目标水平以下。SwarmShare的移动性弹性也在没有精确的UAV位置信息的共存网络中得到验证。
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 to 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 kept 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.