A Mathematical Theory of Network Interference and Its Applications

By Moe Z. Win, Pedro C. Pinto, L. Shepp. Proceedings of the IEEE, 2009

654 citations recorded by the source corpus.

In this paper, we introduce a mathematical framework for the characterization of network interference in wireless systems. We consider a network in which the interferers are scattered according to a spatial Poisson process and are operating asynchronously in a wireless environment subject to path loss, shadowing, and multipath fading. We start by determining the statistical distribution of the aggregate network interference. We then investigate four applications of the proposed model: 1) interference in cognitive radio networks; 2) interference in wireless packet networks; 3) spectrum of the aggregate radio-frequency emission of wireless networks; and 4) coexistence between ultrawideband and narrowband systems. Our framework accounts for all the essential physical parameters that affect network interference, such as the wireless propagation effects, the transmission technology, the spatial density of interferers, and the transmitted power of the interferers.

Subfields: Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/e2fae610cc90d57cfbab3259a88174998ab0b1c6

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Citations in the corpus

Femtosecond-Long Pulse-Based Modulation for Terahertz Band Communication in Nanonetworks (2014, neutral): “For example, an extensive review of the existing models can be found in [38], [39], [40].”
Modeling and Analysis of Cellular Networks Using Stochastic Geometry: A Tutorial (2016, background): “Motivated by its tractability, attempts to promote SG to model cellular networks can be traced back to the late 90’s [34], [35].”
Energy Efficient Heterogeneous Cellular Networks (2013, background): “However, for sparse deployment of MBSs, it is expected that we need to maintain the coverage of the cellular networks when we implement sleeping mode in MBSs either through power control or open access small cells.”
Heterogeneous Cellular Networks with Flexible Cell Association: A Comprehensive Downlink SINR Analysis (2011, methodology): “In the interim, the PPP model has been used extensively for modeling unplanned networks such as femtocells [17], [18], [19] or ad hoc networks [20], [21]; and for both it is now fairly well-accepted [22], [23].”
Stochastic Geometry for Modeling, Analysis, and Design of Multi-Tier and Cognitive Cellular Wireless Networks: A Survey (2013, methodology): “For interferencelimited networks (i.e., Iagg W ), the effect of noise can be ignored and the cdf reduces to FSINR(θ) = 1− LIagg (s) ∣∣ s=cθ , in which the Laplace transform of the aggregate interference is evaluated at some constant c multiplied by the parameter θ of the cdf of SINR.”
A Machine Learning Approach to Ranging Error Mitigation for UWB Localization (2012, background): “However, practical deployment of UWB systems still faces a number of technical challenges, including signal acquisition [30]–[33], multi-user interference [34]–[38], multipath effects [39]–[42], and non-line-of-sight (NLOS) propagation [42]–[45].”
Cognitive Network Interference (2011, background): “However, as shown in [14], the aggregate interference converges to a stable distribution only if the interferers are scattered in the entire plane.”
NLOS identification and mitigation for localization based on UWB experimental data (2010, neutral): “acquisition [16], multi-user interference [17], [18], multipath effects [19], [20], and non-line-of-sight (NLOS) propagation [10], [11].”