Journal
IEEE-ACM TRANSACTIONS ON NETWORKING
Volume 21, Issue 4, Pages 1298-1308Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNET.2012.2222923
Keywords
Channel allocation; learning automata; network coding; stochastic processes; TDMA; wireless networks
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Funding
- Science Foundation Ireland [07/IN.1/I901]
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We show that several important resource allocation problems in wireless networks fit within the common framework of constraint satisfaction problems (CSPs). Inspired by the requirements of these applications, where variables are located at distinct network devices that may not be able to communicate but may interfere, we define natural criteria that a CSP solver must possess in order to be practical. We term these algorithms decentralized CSP solvers. The best known CSP solvers were designed for centralized problems and do not meet these criteria. We introduce a stochastic decentralized CSP solver, proving that it will find a solution in almost surely finite time, should one exist, and also showing it has many practically desirable properties. We benchmark the algorithm's performance on a well-studied class of CSPs, random k-SAT, illustrating that the time the algorithm takes to find a satisfying assignment is competitive with stochastic centralized solvers on problems with order a thousand variables despite its decentralized nature. We demonstrate the solver's practical utility for the problems that motivated its introduction by using it to find a noninterfering channel allocation for a network formed from data from downtown Manhattan.
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