空天地一体化网络中的分层认知频谱共享
Hierarchical Cognitive Spectrum Sharing in Space-Air-Ground Integrated Networks
AI总结：
针对空天地一体化网络中传统认知频谱共享架构无法满足不同网络差异化QoS需求的问题，提出分层认知频谱共享架构及基于惩罚的迭代波束成形等方案，有效提升了频谱利用效率与网络性能。
摘要：
在空天地一体化网络（SAGINs）中，认知频谱共享被视为提升频谱效率的有效方案，它允许次级网络接入主网络的频谱。然而，SAGIN中的不同网络可能具有不同的服务质量（QoS）需求，传统认知频谱共享架构无法很好地满足这些需求。例如，空中网络通常有较高的QoS要求，但当其作为次级网络时可能无法达标。为解决这一问题，本文提出了一种面向SAGIN的分层认知频谱共享架构（HCSSA），将次级网络分为优先次级网络和普通次级网络。具体而言，空中网络和地面网络可在对卫星终端造成的干扰低于特定阈值的条件下，接入卫星网络的频谱。此外，考虑到空中网络的优先级高于地面网络，我们旨在通过速率约束保障空中网络的性能。在这两个约束条件下，我们通过联合优化空中基站和地面基站的发射波束成形向量，实现地面网络的和速率最大化。为求解这一非凸问题，我们提出了一种基于惩罚的迭代波束成形（PIBF）方案，该方案结合了惩罚法和连续凸逼近技术。此外，我们还通过优化归一化波束成形向量和功率控制，设计了三种低复杂度方案。最后，我们开展了大量数值仿真，对比了所提PIBF方案与低复杂度方案的性能。结果同时表明，与传统认知频谱共享架构相比，所提HCSSA具有显著优势。
英文摘要：
In space-air-ground integrated networks (SAGINs), cognitive spectrum sharing has been regarded as a promising solution to improve spectrum efficiency by enabling a secondary network to access the spectrum of a primary network. However, different networks in SAGIN may have different quality of service (QoS) requirements, which can not be well satisfied with the traditional cognitive spectrum sharing architecture. For example, the aerial network typically has high QoS requirements, which however may not be met when it acts as a secondary network. To address this issue, in this paper, we propose a hierarchical cognitive spectrum sharing architecture (HCSSA) for SAGINs, where the secondary networks are divided into a preferential one and an ordinary one. Specifically, the aerial and terrestrial networks can access the spectrum of the satellite network under the condition that the caused interference to the satellite terminal is below a certain threshold. Besides, considering that the aerial network has a higher priority than the terrestrial network, we aim to use a rate constraint to ensure the performance of the aerial network. Subject to these two constraints, we consider a sum-rate maximization for the terrestrial network by jointly optimizing the transmit beamforming vectors of the aerial and terrestrial base stations. To solve this non-convex problem, we propose a penalty-based iterative beamforming (PIBF) scheme that uses the penalty method and the successive convex approximation technique. Moreover, we also develop three low-complexity schemes by optimizing the normalized beamforming vectors and power control. Finally, we provide extensive numerical simulations to compare the performance of the proposed PIBF scheme and the low-complexity schemes. The results also demonstrate the advantages of the proposed HCSSA compared with the traditional cognitive spectrum sharing architecture.
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