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.

中文翻译：

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无线网络时空性能的随机几何分析：教程


无线网络的性能从根本上受到总干扰的限制，而总干扰取决于干扰源的空间分布、信道条件和用户流量模式（或排队动态）。这些因素通常表现出空间和时间相关性，从而使得大规模网络的性能依赖于环境（即依赖于网络拓扑、阻塞位置等）。可以在协议设计（例如，频谱、负载、位置、能量感知资源分配）中利用相关性来提供高效的无线服务。为此，需要具有时空相关性的准确系统级性能表征和评估。在这种背景下，随机几何模型和随机图技术已被用来开发分析框架，以捕获大规模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的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）。我们首先讨论时空性能分析的重要性、影响 SIR 中时空相关性的不同参数以及时空分析的不同性能指标。然后，我们描述了表征不同网络配置（独立、吸引、排斥配置）、阴影场景、用户位置、排队行为、中继、重传和移动性的时空 SIR 相关性的方法。 最后，我们概述了新兴无线通信场景时空分析背景下的未来研究方向。