Convergence of Distributed Learning Algorithms for Optimal Wireless Channel Allocation.
Leith, D. J. and Clifford, P. (2006) Convergence of Distributed Learning Algorithms for Optimal Wireless Channel Allocation. In: 45th IEEE Conference on Decision & Control Manchester Grand Hyatt Hotel San Diego, CA, USA, December 13-15, 2006. IEEE, pp. 2980-2985. ISBN 1-4244-0171-2
In this paper we establish the convergence to an optimal non-interfering channel allocation of a class of
distributed stochastic algorithms. We illustrate the application of this result via (i) a communication-free distributed learning strategy for wireless channel allocation and (ii) a distributed learning strategy that can opportunistically exploit communication between nodes to improve convergence speed while retaining guaranteed convergence in the absence of communication.
|Additional Information:||"©2006 IEEE. Reprinted from the 45th IEEE Conference on Decision & Control Manchester Grand Hyatt Hotel San Diego, CA, USA, December 13-15, 2006. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE." http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4177619&isnumber=4176993|
|Keywords:||Channel allocation; Convergence; Learning automata; Optical communication; Wireless LAN; Communication-free distributed learning; Convergence speed; Distributed stochastic algorithms; Optimal noninterfering channel allocation; Optimal wireless channel allocation; CDC 2006; Hamilton Institute. |
|Subjects:||Science & Engineering > Hamilton Institute|
Science & Engineering > Computer Science
|Deposited By:||Hamilton Editor|
|Deposited On:||12 Jan 2010 11:42|
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