Abstract
This paper addresses opportunistic spectrum access (OSA)
in non-cooperative cognitive radio networks (CRNs). The
selfish behaviors of the secondary users (SUs) will cause
a CRN to collapse. The SUs are thus enabled to build
beliefs about how other SUs would respond to their
decision makings. The interaction among the SUs is
modeled as a stochastic learning process. In this way,
each SU can independently learn the behaviors of the
competitors, optimize the OSA strategies, and finally
achieve the goal of reciprocity. Two learning algorithms
are proposed to stabilize the stochastic CRNs, the
convergence properties of which are also proven
theoretically. Simulation results validate the
performance of the proposed results, and show that the
achieved system performance outperforms some existing
protocols.
Original language | English |
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Title of host publication | Proceedings |
Subtitle of host publication | 8th International Conference on Cognitive Radio Oriented Wireless Networks, CROWNCOM 2013 |
Publisher | IEEE Institute of Electrical and Electronic Engineers |
Pages | 202-207 |
ISBN (Electronic) | 978-1-4799-2120-1 |
DOIs | |
Publication status | Published - 2013 |
MoE publication type | A4 Article in a conference publication |
Event | 8th International Conference on Cognitive Radio Oriented Wireless Networks and Communications, CROWNCOM 2013 - Washington, United States Duration: 8 Jul 2013 → 10 Jul 2013 Conference number: 8 |
Conference
Conference | 8th International Conference on Cognitive Radio Oriented Wireless Networks and Communications, CROWNCOM 2013 |
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Abbreviated title | CROWNCOM 2013 |
Country | United States |
City | Washington |
Period | 8/07/13 → 10/07/13 |