Closing the Gap in the Capacity of Wireless Networks Via Percolation Theory

By Massimo Franceschetti, Olivier Dousse, David Tse, Patrick Thiran. IEEE Transactions on Information Theory, 2007

692 citations recorded by the source corpus.

An achievable bit rate per source-destination pair in a wireless network of n randomly located nodes is determined adopting the scaling limit approach of statistical physics. It is shown that randomly scattered nodes can achieve, with high probability, the same 1/radicn transmission rate of arbitrarily located nodes. This contrasts with previous results suggesting that a 1/radicnlogn reduced rate is the price to pay for the randomness due to the location of the nodes. The network operation strategy to achieve the result corresponds to the transition region between order and disorder of an underlying percolation model. If nodes are allowed to transmit over large distances, then paths of connected nodes that cross the entire network area can be easily found, but these generate excessive interference. If nodes transmit over short distances, then such crossing paths do not exist. Percolation theory ensures that crossing paths form in the transition region between these two extreme scenarios. Nodes along these paths are used as a backbone, relaying data for other nodes, and can transport the total amount of information generated by all the sources. A lower bound on the achievable bit rate is then obtained by performing pairwise coding and decoding at each hop along the paths, and using a time division multiple access scheme

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/5c2418e5ff27afff0ac0177f0b8dd11a2766c4be

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Citations in the corpus

Downlink Cellular Network Analysis With Multi-Slope Path Loss Models (2014, background): “,[2], [19]–[21], and with experimental support seen in [22].”
Beta Processes, Stick-Breaking and Power Laws (2011, background): “Then [Franceschetti et al., 2007] the same tail bound as in Eq.”
Physical-layer network coding: Tutorial, survey, and beyond (2011, methodology): “We propose a transmission scheme inspired by [106] and [107].”
Hierarchical Cooperation Achieves Optimal Capacity Scaling in Ad Hoc Networks (2006, background): “Note how dramatically different is this new linear capacity scaling law from the well-known throughput scaling of implied by [3], [4] for the same model.”
Cognitive radio networks (2008, neutral): “When the nodes are distributed according to a Poisson point process, however, a backbone-based routing scheme achieves the per node scaling of 1 / √ n [28], meeting the upper bound.”