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Mathematical Problems in Engineering
Volume 2013 (2013), Article ID 727310, 10 pages
Research Article

Performance Analysis and Optimization of an Adaptive Admission Control Scheme in Cognitive Radio Networks

1College of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China
2Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Yanshan University, Qinhuangdao 066004, China
3Department of Intelligence and Informatics, Konan University, Kobe 658-8501, Japan
4Department of Networked Systems and Services, Budapest University of Technology and Economics, Budapest 1521, Hungary

Received 1 August 2013; Revised 6 October 2013; Accepted 10 October 2013

Academic Editor: Pui-Sze Chow

Copyright © 2013 Shunfu Jin 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.


In cognitive radio networks, if all the secondary user (SU) packets join the system without any restrictions, the average latency of the SU packets will be greater, especially when the traffic load of the system is higher. For this, we propose an adaptive admission control scheme with a system access probability for the SU packets in this paper. We suppose the system access probability is inversely proportional to the total number of packets in the system and introduce an Adaptive Factor to adjust the system access probability. Accordingly, we build a discrete-time preemptive queueing model with adjustable joining rate. In order to obtain the steady-state distribution of the queueing model exactly, we construct a two-dimensional Markov chain. Moreover, we derive the formulas for the blocking rate, the throughput, and the average latency of the SU packets. Afterwards, we provide numerical results to investigate the influence of the Adaptive Factor on different performance measures. We also give the individually optimal strategy and the socially optimal strategy from the standpoints of the SU packets. Finally, we provide a pricing mechanism to coordinate the two optimal strategies.