A near optimal policy for channel allocation in cognitive radio

Sarah Filippi; Olivier Cappé; Fabrice Clérot; Eric Moulines

doi:10.1007/978-3-540-89722-4_6

A near optimal policy for channel allocation in cognitive radio

Sarah Filippi
, Olivier Cappé
, Fabrice Clérot
, Eric Moulines

CNRS LTCI
Orange Labs

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	Recent Advances in Reinforcement Learning - 8th European Workshop, EWRL 2008, Revised and Selected Papers
Pages	69-81
Number of pages	13
DOIs	https://doi.org/10.1007/978-3-540-89722-4_6
Publication status	Published - 1 Dec 2008
Externally published	Yes
Event	8th European Workshop on Reinforcement Learning, EWRL 2008 - Villeneuve d'Ascq, France Duration: 30 Jun 2008 → 3 Jul 2008

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	5323 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	8th European Workshop on Reinforcement Learning, EWRL 2008
Country/Territory	France
City	Villeneuve d'Ascq
Period	30/06/08 → 3/07/08

Access to Document

10.1007/978-3-540-89722-4_6

Cite this

Filippi, S., Cappé, O., Clérot, F., & Moulines, E. (2008). A near optimal policy for channel allocation in cognitive radio. In Recent Advances in Reinforcement Learning - 8th European Workshop, EWRL 2008, Revised and Selected Papers (pp. 69-81). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5323 LNAI). https://doi.org/10.1007/978-3-540-89722-4_6

Filippi, Sarah ; Cappé, Olivier ; Clérot, Fabrice et al. / A near optimal policy for channel allocation in cognitive radio. Recent Advances in Reinforcement Learning - 8th European Workshop, EWRL 2008, Revised and Selected Papers. 2008. pp. 69-81 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{24c1c1a360f24254933b899cd3b0aab6,

title = "A near optimal policy for channel allocation in cognitive radio",

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.",

author = "Sarah Filippi and Olivier Capp{\'e} and Fabrice Cl{\'e}rot and Eric Moulines",

year = "2008",

month = dec,

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doi = "10.1007/978-3-540-89722-4\_6",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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Filippi, S, Cappé, O, Clérot, F & Moulines, E 2008, A near optimal policy for channel allocation in cognitive radio. in Recent Advances in Reinforcement Learning - 8th European Workshop, EWRL 2008, Revised and Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5323 LNAI, pp. 69-81, 8th European Workshop on Reinforcement Learning, EWRL 2008, Villeneuve d'Ascq, France, 30/06/08. https://doi.org/10.1007/978-3-540-89722-4_6

A near optimal policy for channel allocation in cognitive radio. / Filippi, Sarah; Cappé, Olivier; Clérot, Fabrice et al.
Recent Advances in Reinforcement Learning - 8th European Workshop, EWRL 2008, Revised and Selected Papers. 2008. p. 69-81 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5323 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - 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.

AB - 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.

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Filippi S, Cappé O, Clérot F, Moulines E. A near optimal policy for channel allocation in cognitive radio. In Recent Advances in Reinforcement Learning - 8th European Workshop, EWRL 2008, Revised and Selected Papers. 2008. p. 69-81. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-89722-4_6

A near optimal policy for channel allocation in cognitive radio

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