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| Presentation | 2013-03-01 14:00 A Reinforcement Learning Based Sensing Policy for Cognitive Radio Systems Fereidoun H. Panahi, Tomoaki Ohtsuki (Keio Univ.)  RCS2012-363 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Spectrum sensing is a critical function for enabling dynamic spectrum access (DSA) in wireless networks that utilize cognitive radio (CR). In DSA networks, an unlicensed secondary user can gain access to a licensed spectrum band as long as it does not cause harmful interference to primary user (PU). As a result, an efficient sensing scheme is essential for the secondary user in making sound opportunistic spectrum access decisions in a cognitive radio network. In this report, we present a learning based scheme for spectrum (channel) sensing in CR network. Specifically, we formulate the channel sensing problem as a partially observable Markov decision process (POMDP), where the most likely channel state is derived by a learning process called Fuzzy Q-Learning (FQL). Then, we use Baum-Welch Algorithm (BWA) for Markov sources in noise to estimate channel state transition probabilities of the PU which are used in the POMDP framework. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Cognitive Radio (CR) / Reinforcement learning (RL) / Baum-Welch Algorithm (BWA) / / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 112, no. 443, RCS2012-363, pp. 471-476, Feb. 2013. | |
| Paper # | RCS2012-363 | |
| Date of Issue | 2013-02-20 (RCS) | |
| ISSN | Print edition: ISSN 0913-5685 Online edition: ISSN 2432-6380 | |
| Copyright and reproduction |
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) | |
| Download PDF | RCS2012-363 |
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| Conference Information | |
| Committee | RCS SR SRW |
| Conference Date | 2013-02-27 - 2013-03-01 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Waseda Univ. |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Mobile Communication Workshop |
| Paper Information | |
| Registration To | RCS |
| Conference Code | 2013-02-RCS-SR-SRW |
| Language | English |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | A Reinforcement Learning Based Sensing Policy for Cognitive Radio Systems |
| Sub Title (in English) | |
| Keyword(1) | Cognitive Radio (CR) |
| Keyword(2) | Reinforcement learning (RL) |
| Keyword(3) | Baum-Welch Algorithm (BWA) |
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| 1st Author's Name | Fereidoun H. Panahi |
| 1st Author's Affiliation | Keio University (Keio Univ.) |
| 2nd Author's Name | Tomoaki Ohtsuki |
| 2nd Author's Affiliation | Keio University (Keio Univ.) |
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| Speaker | Author-1 |
| Date Time | 2013-03-01 14:00:00 |
| Presentation Time | 20 minutes |
| Registration for | RCS |
| Paper # | RCS2012-363 |
| Volume (vol) | vol.112 |
| Number (no) | no.443 |
| Page | pp.471-476 |
| #Pages | 6 |
| Date of Issue | 2013-02-20 (RCS) |
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