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Distributed Algorithms for Approximating Wireless Network Capacity
| Citations: | 19 - 2 self |
BibTeX
@MISC{Dinitz_distributedalgorithms,
author = {Michael Dinitz},
title = {Distributed Algorithms for Approximating Wireless Network Capacity},
year = {}
}
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Abstract
Abstract—In this paper we consider the problem of maximizing wireless network capacity (a.k.a. one-shot scheduling) in both the protocol and physical models. We give the first distributed algorithms with provable guarantees in the physical model, and show how they can be generalized to more complicated metrics and settings in which the physical assumptions are slightly violated. We also give the first algorithms in the protocol model that do not assume transmitters can coordinate with their neighbors in the interference graph, so every transmitter chooses whether to broadcast based purely on local events. Our techniques draw heavily from algorithmic game theory and machine learning theory, even though our goal is a distributed algorithm. Indeed, our main results allow every transmitter to run any algorithm it wants, so long as its algorithm has a learning-theoretic property known as no-regret in a game-theoretic setting. I.
Keyphrases
approximating wireless network capacity physical model machine learning theory first algorithm provable guarantee distributed algorithm learning-theoretic property wireless network capacity protocol model physical assumption main result local event game-theoretic setting algorithmic game theory complicated metric one-shot scheduling interference graph
