Interference Model Similarity Index and Its Applications to Millimeter-Wave Networks
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1710.02659.pdf
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647.51 KB
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Author(s)
Shokri-Ghadikolaei, Hossein
Fischione, Carlo
Modiano, Eytan H
Date Issued
January 2018
Journal
IEEE Transactions on Wireless Communications
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Shokri-Ghadikolaei, Hossein et al. “Interference Model Similarity Index and Its Applications to Millimeter-Wave Networks.” IEEE Transactions on Wireless Communications 17, 1 (January 2018): 71–85 © 2017 IEEE
Version
Author's final manuscript
Abstract
In wireless communication networks, interference models are routinely used for tasks, such as performance analysis, optimization, and protocol design. These tasks are heavily affected by the accuracy and tractability of the interference models. Yet, quantifying the accuracy of these models remains a major challenge. In this paper, we propose a new index for assessing the accuracy of any interference model under any network scenario. Specifically, it is based on a new index that quantifies the ability of any interference model in correctly predicting harmful interference events, that is, link outages. We consider specific wireless scenario of both conventional sub-6 GHz and millimeter-wave networks and demonstrate how our index yields insights into the possibility of simplifying the set of dominant interferers, replacing a Nakagami or Rayleigh random fading by an equivalent deterministic channel, and ignoring antenna sidelobes. Our analysis reveals that in highly directional antenna settings with obstructions, even simple interference models (such as the classical protocol model) are accurate, while with omnidirectional antennas, more sophisticated and complex interference models (such as the classical physical model) are necessary. Our new approach makes it possible to adopt the simplest interference model of adequate accuracy for every wireless network.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
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Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/TWC.2017.2762667
