Autonomic management of transmission resources via reinforcement learning for cognitive radio.
Published: 2007
Language: eng
Number of pages: 110
ISBN-13: 9780494274637
Description: We explore the use of cognitive radio in the autonomic management of radio resources in market-based environments. To do so, we design two components: a resource allocation scheme for physical layer (PHY) parameters of bandwidth and power, and a machine-learning platform based on the reinforcement learning (RL) algorithms of Q-learning and Dyna. We integrate these components together to create a system where transmitters charge independent service prices for resources. Nodes incrementally learn to charge appropriate prices and cooperate to perform efficient spectrum sharing.This thesis evaluates our resource control algorithm by presenting a learning improvement over the non-cooperative tragedy of the commons where nodes perform resource hoarding. While we show that the algorithm converges slowly, we explore the potential need for more directed learning algorithms based on guided machine learning (GML) to improve the convergence speed of our algorithm.
Notes: Source: Masters Abstracts International, Volume: 45-06, page: 3256.

Thesis (M.A.Sc.)--University of Toronto, 2007.

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