PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Latent state models of primary user behavior for opportunistic spectrum access
Joni Pajarinen, Jaakko Peltonen, Mikko A. Uusitalo and Ari Hottinen
In: PIMRC'09, the IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, 13-16 Sep 2009, Tokyo, Japan.


Opportunistic spectrum access, where cognitive radio devices detect available unused radio channels and exploit them for communication, avoiding collisions with existing users of the channels, is a central topic of research for future wireless communication. When each device has limited resources to sense which channels are available, the task becomes a reinforcement learning problem that has been studied with partially observable Markov decision processes (POMDPs). However, current POMDP solutions are based on simplistic representations where channels are simply on/off (transmitting or idle). We show that more complicated Markov models where on/off states are part of complicated behavior of the channel owner (primary user) yield better POMDPs achieving more successful transmissions and less collisions. Copyright 2009 IEEE. 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. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.

EPrint Type:Conference or Workshop Item (Paper)
Additional Information:
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Theory & Algorithms
ID Code:6417
Deposited By:Jaakko Peltonen
Deposited On:08 March 2010