A channel selection mechanism based on incumbent appearance expectation for cognitive networks

Authors:
Kaveh Ghaboosi;Allen B. MacKenzie;Luiz A. DaSilva;Abdallah S. Abdallah;Matti Latva-aho
Affiliations:
Centre for Wireless Communications, University of Oulu, Finland;Department of Electrical and Computer Engineering, Virginia Tech.;Department of Electrical and Computer Engineering, Virginia Tech,;Department of Electrical and Computer Engineering, Virginia Tech,;Centre for Wireless Communications, University of Oulu, Finland
Venue:
WCNC'09 Proceedings of the 2009 IEEE conference on Wireless Communications & Networking Conference
Year:
2009

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Abstract

In this paper, we investigate stochastic multi-channel load balancing in a distributed cognitive network coexisting with primary users. In particular, we propose a probabilistic technique for traffic distribution among a set of data channels by incorporating statistical information of primary users' activities in different channels into the selection process without centralized control. Moreover, the proposed scheme is enabled by a multi-channel binary exponential backoff mechanism to further facilitate contention resolution in a multi-channel environment. It is shown through simulations that the proposed MAC layer enhancement outperforms well-known multi-channel MAC protocols both in terms of aggregate end-to-end throughput and average frame end-to-end delay. Furthermore, its performance is also compared to two heuristic channel selection techniques in a multi-channel cognitive network, coexisting with incumbents.