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Wireless Communications and Mobile Computing
Volume 2017 (2017), Article ID 7385627, 12 pages
https://doi.org/10.1155/2017/7385627
Research Article

Resource Allocation in Heterogeneous Buffered Cognitive Radio Networks

1Department of Electrical, Electronic and Computer Engineering, University of Pretoria, Pretoria 0002, South Africa
2Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, MB, Canada R3T 5V6

Correspondence should be addressed to B. S. Awoyemi; moc.liamg@ednutababimeyowa

Received 9 May 2017; Revised 9 June 2017; Accepted 20 June 2017; Published 25 July 2017

Academic Editor: Gonzalo Vazquez-Vilar

Copyright © 2017 B. S. Awoyemi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Resources available for operation in cognitive radio networks (CRN) are generally limited, making it imperative for efficient resource allocation (RA) models to be designed for them. However, in most RA designs, a significant limiting factor to the RA’s productivity has hitherto been mostly ignored, the fact that different users or user categories do have different delay tolerance profiles. To address this, in this paper, an appropriate RA model for heterogeneous CRN with delay considerations is developed and analysed. In the model, the demands of users are first categorised and then, based on the distances of users from the controlling secondary user base station and with the assumption that the users are mobile, the user demands are placed in different queues having different service capacities and the resulting network is analysed using queueing theory. Furthermore, to achieve optimality in the RA process, an important concept is introduced whereby some demands from one queue are moved to another queue where they have a better chance of enhanced service, thereby giving rise to the possibility of an improvement in the overall performance of the network. The performance results obtained from the analysis, particularly the blocking probability and network throughput, show that the queueing model incorporated into the RA process can help in achieving optimality for the heterogeneous CRN with buffered data.