Abstract
| Original language | English |
|---|---|
| Title of host publication | Proceedings |
| Subtitle of host publication | 8th International Conference on Cognitive Radio Oriented Wireless Networks, CROWNCOM 2013 |
| Publisher | Institute of Electrical and Electronic Engineers IEEE |
| 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 |
|---|---|
| Abbreviated title | CROWNCOM 2013 |
| Country | United States |
| City | Washington |
| Period | 8/07/13 → 10/07/13 |
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Reciprocity inspired learning for opportunistic spectrum access in cognitive radio networks. / Chen, Xianfu; Chen, Tao; Cheng, W.; Zhang, H.
Proceedings: 8th International Conference on Cognitive Radio Oriented Wireless Networks, CROWNCOM 2013 . Institute of Electrical and Electronic Engineers IEEE, 2013. p. 202-207.Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review
TY - GEN
T1 - Reciprocity inspired learning for opportunistic spectrum access in cognitive radio networks
AU - Chen, Xianfu
AU - Chen, Tao
AU - Cheng, W.
AU - Zhang, H.
N1 - Project code: 82164
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
U2 - 10.1109/CROWNCom.2013.6636818
DO - 10.1109/CROWNCom.2013.6636818
M3 - Conference article in proceedings
SP - 202
EP - 207
BT - Proceedings
PB - Institute of Electrical and Electronic Engineers IEEE
ER -