Title :
Rank-optimal channel selection strategy in cognitive networks
Author :
Torabi, Nasser ; Rostamzadeh, Karim ; Leung, Victor C. M.
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of British Columbia, Vancouver, BC, Canada
Abstract :
A learning strategy for distributed channel selection in Cognitive Radio networks is proposed. This strategy helps quality of service (QoS) provisioning such that competing secondary users cooperatively converge to their rank-optimal channels while channel availability statistics are initially unknown. By this convergence, collision reaches zero since users eventually work on their own orthogonal channels. The proposed learning strategy, kth-MAB, is inspired from the Multi-Armed Bandit problem but it converges to the kth best arm. The rank-optimal channel for each user is identified based on the user´s QoS demands. We believe that this learning and allocation policy provides a better level of QoS for secondary users since evaluation results represent order optimality in terms of the average throughput.
Keywords :
cognitive radio; learning (artificial intelligence); quality of service; radio networks; wireless channels; QoS; average throughput; channel availability statistics; cognitive radio network; learning strategy; multiarmed bandit problem; orthogonal channel; policy allocation; quality of service; rank-optimal distributed channel selection strategy; secondary user;
Conference_Titel :
Global Communications Conference (GLOBECOM), 2012 IEEE
Conference_Location :
Anaheim, CA
Print_ISBN :
978-1-4673-0920-2
Electronic_ISBN :
1930-529X
DOI :
10.1109/GLOCOM.2012.6503147