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Fundamentals of Wireless Network Analysis

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Stochastic Geometry Analysis of Multi-Antenna Wireless Networks

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Abstract

In this chapter, the fundamentals of wireless network analysis via stochastic geometry are introduced. The Poisson network model is first presented, and key performance metrics in wireless networks are defined. By modeling a wireless network as a Poisson point process, the distribution of the aggregate interference is characterized using the Laplace transform, which is a key analytical step leading to tractable results of the signal-to-interference-plus-noise ratio (SINR) distribution. Sample results are presented for coverage and rate analysis in single-antenna cellular and ad hoc networks.

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Notes

  1. 1.

    The mathematical definition of Lebesgue measure is not provided here but can be well found in [21, 24], while there are some special cases for d that are easy to understand and will be further used in this monograph. When \(d=1\), \(v_1(\cdot )=l\) is the length measure; when \(d=2\), \(v_2(\cdot )=A\) is the area measure; when \(d=3\), \(v_3(\cdot )=V\) is the volume measure.

  2. 2.

    Here we pick the two-dimensional Poisson hole process as the example for the ease of presentation.

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Authors and Affiliations

  1. Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Hong Kong, China

    Xianghao Yu, Chang Li & Khaled B. Letaief

  2. Department of Electronic and Information Engineering, Hong Kong Polytechnic University, Kowloon, Hong Kong, China

    Jun Zhang

Authors
  1. Xianghao Yu
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  2. Chang Li
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  3. Jun Zhang
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  4. Khaled B. Letaief
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Yu, X., Li, C., Zhang, J., Letaief, K.B. (2019). Fundamentals of Wireless Network Analysis. In: Stochastic Geometry Analysis of Multi-Antenna Wireless Networks. Springer, Singapore. https://doi.org/10.1007/978-981-13-5880-7_2

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  • DOI: https://doi.org/10.1007/978-981-13-5880-7_2

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