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A Caltech Library Repository Feedhttp://www.rssboard.org/rss-specificationpython-feedgenenFri, 08 Dec 2023 12:14:41 +0000Throughput scaling in random wireless networks
https://resolver.caltech.edu/CaltechAUTHORS:20150209-075258412
Authors: Gowaikar, Radhika; Hochwald, Bertrand; Hassibi, Babak
Year: 2015
We propose and analyze two models of networks in which pairs of nodes communicate over a shared wireless medium. We are interested in the maximum total aggregate traffic flow that is possible through the network. Our first model differs substantially from most existing models in that the channel connections in our network are entirely random: we assume that, rather than being governed by geometry and a decay law, the strength of the connections between nodes is drawn independently from a common distribution. The next model is more general and works at two scales. At a local scale, characterized by nodes being within a distance r from each other, connections are drawn independently from some distribution, but at a global scale, characterized by nodes being further apart from each other than a distance r, channel connections are governed by a Rayleigh distribution, with the power satisfying a distance-based decay law.
For both models we show that an appropriate distribution for the channel strengths and other parameters can give a throughput that scales almost linearly in the number of nodes of the network. This is a significant improvement over the square-root scaling that has been shown in several previous works.https://authors.library.caltech.edu/records/w7r4z-6mx66