海上认知卫星-无人机-地面网络的功率分配

Xinran Fang, Yanmin Wang, W. Feng, Yunfei Chen, B. Ai
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引用次数: 5

摘要

在本文中,我们研究了混合卫星-无人机(UAV)-地面网络的海上覆盖增强。我们采用系留无人机提供空中基站(BS)站点,并以用户为中心的方式编排陆上和无人机安装的基站。为了解决频谱稀缺问题,所有可用频谱在卫星、无人机和地面基站(tbs)之间共享。这在不规则无单元系统拓扑下产生了不希望的挑战性同信道干扰(CCI)。我们建立了一个认知框架,不仅可以感知频谱状态,还可以感知船舶的位置信息。根据认知信息,以用户为中心的虚拟集群由一组无人机和陆上BSs自组织。此外,通过认知位置信息可以获得位置相关的大尺度通道状态信息(CSI)。因此,我们优化了仅使用大规模CSI的功率分配策略,以进一步减轻对卫星用户的簇间干扰和泄漏干扰。这个问题是非凸的。利用随机矩阵理论和连续凸优化方法,采用迭代法求解。仿真结果验证了所提出的认知框架和功率分配方案的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Power Allocation for Maritime Cognitive Satellite-UAV-Terrestrial Networks
In this paper, we investigate hybrid satellite-unmanned aerial vehicle (UAV)-terrestrial networks for maritime coverage enhancement. We adopt tethered UAVs to provide aerial base station (BS) sites, and orchestrate onshore and UAV-mounted BSs in a user-centric manner. To address the spectrum scarcity problem, all available spectrum is shared among satellites, UAVs and terrestrial base stations (TBSs). This generates undesirable challenging co-channel interference (CCI) under the irregular cell-free system topology. We establish a cognitive framework to sense not only the spectrum status but also ships’ position information. According to the cognitive information, user-centric virtual clusters are self organized by a group of UAVs and onshore BSs. Besides, location-dependent large-scale channel state information (CSI) can be obtained through the cognitive position information. We thus optimize the power allocation strategy with only the large-scale CSI, to further mitigate both the inter-cluster interference and leakage interference to satellite users. The problem is non-convex. By using the random matrix theory and successive convex optimization methods, we solve it in an iterative way. Simulation results corroborate the efficiency of the proposed cognitive framework and the presented power allocation scheme.
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