Stochastic Geometry Analysis of Spatial-Temporal Performance in Wireless Networks: A Tutorial
Stochastic Geometry Analysis of Spatial-Temporal Performance in Wireless Networks: A Tutorial
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DOI：
10.1109/comst.2021.3104581
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发表时间：
2021-01-01
影响因子：
35.6
通讯作者：
Jiang, Hai
中科院分区：
文献类型：
--
作者：
Lu, Xiao;Salehi, Mohammad;Jiang, Hai
无线网络的性能从根本上受到聚合干扰的限制，聚合干扰取决于干扰源的空间分布、信道条件和用户业务模式（或干扰动态）。这些因素通常表现出空间和时间的相关性，从而使大规模网络的性能依赖于环境（即，取决于网络拓扑、阻塞的位置等）。可以在协议设计中利用该相关性（例如，频谱、负载、位置、能量感知资源分配）以提供高效的无线服务。为此，需要精确的系统级性能表征和时空相关性评估。在这种情况下，随机几何模型和随机图技术已被用来开发分析框架，以捕捉在大规模的无线网络中的时空干扰相关性。本文的目的是提供有关大规模无线网络随机几何分析的教程，该分析可捕获时空干扰相关性（从而捕获信号干扰比（SIR）相关性）。我们首先讨论了时空性能分析的重要性，不同的参数影响的空间-时间相关性的SIR，和不同的性能指标的空间-时间分析。然后，我们描述的方法来表征不同的网络配置（独立的，有吸引力的，排斥的配置），阴影的情况下，用户的位置，路由行为，中继，重传，和移动性的时空SIR相关性。最后，我们概述了未来的研究方向的背景下，新兴的无线通信场景的时空分析。
The performance of wireless networks is fundamentally limited by the aggregate interference, which depends on the spatial distributions of the interferers, channel conditions, and user traffic patterns (or queueing dynamics). These factors usually exhibit spatial and temporal correlations and thus make the performance of large-scale networks environment-dependent (i.e., dependent on network topology, locations of the blockages, etc.). The correlation can be exploited in protocol designs (e.g., spectrum-, load-, location-, energy-aware resource allocations) to provide efficient wireless services. For this, accurate system-level performance characterization and evaluation with spatial-temporal correlation are required. In this context, stochastic geometry models and random graph techniques have been used to develop analytical frameworks to capture the spatial-temporal interference correlation in large-scale wireless networks. The objective of this article is to provide a tutorial on the stochastic geometry analysis of large-scale wireless networks that captures the spatial-temporal interference correlation (and hence the signal-to-interference ratio (SIR) correlation). We first discuss the importance of spatial-temporal performance analysis, different parameters affecting the spatial-temporal correlation in the SIR, and the different performance metrics for spatial-temporal analysis. Then we describe the methodologies to characterize spatial-temporal SIR correlations for different network configurations (independent, attractive, repulsive configurations), shadowing scenarios, user locations, queueing behavior, relaying, retransmission, and mobility. We conclude by outlining future research directions in the context of spatial-temporal analysis of emerging wireless communications scenarios.