A Near Optimal Policy for Channel Allocation in Cognitive Radio

Authors:
Sarah Filippi;Olivier Cappé;Fabrice Clérot;Eric Moulines
Affiliations:
LTCI, TELECOM ParisTech and CNRS, Paris, France 75013;LTCI, TELECOM ParisTech and CNRS, Paris, France 75013;France Telecom R&D, Lannion, France 22300;LTCI, TELECOM ParisTech and CNRS, Paris, France 75013
Venue:
Recent Advances in Reinforcement Learning
Year:
2008

Citing 7
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Abstract

Several tasks of interest in digital communications can be cast into the framework of planning in Partially Observable Markov Decision Processes (POMDP). In this contribution, we consider a previously proposed model for a channel allocation task and develop an approach to compute a near optimal policy. The proposed method is based on approximate (point based) value iteration in a continuous state Markov Decision Process (MDP) which uses a specific internal state as well as an original discretization scheme for the internal points. The obtained results provide interesting insights into the behavior of the optimal policy in the channel allocation model.