A New Opportunistic Spectrum Access Approach in Unslotted Primary Systems

DOI

10.2991/iccsee.2013.403How to use a DOI?

Keywords

cognitive radio, opportunistic spectrum access, learning, channel selection

Abstract

To solve the channel selection issue in opportunistic spectrum access, a new Q-Learning based algorithm for channel selection is proposed. The algorithm can lead secondary user to select channels with maximum cumulative reward, and maximize secondary user throughput. A Boltzmann learning rule is adopted to achieve well tradeoff between channel exploration and exploitation. From the simulation results, compared with random selection algorithm, the algorithm does not require prior knowledge or prediction models of the channel environment, yet can still select the optimal channel adaptively, improve the secondary user capability and attain to the convergence in short time.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Kai Zhang
AU  - Ou Li
AU  - Baiwei Yang
AU  - Yang Liu
PY  - 2013/03
DA  - 2013/03
TI  - A New Opportunistic Spectrum Access Approach in Unslotted Primary Systems
BT  - Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013)
PB  - Atlantis Press
SP  - 1605
EP  - 1608
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccsee.2013.403
DO  - 10.2991/iccsee.2013.403
ID  - Zhang2013/03
ER  -