ISSN 1000-1239 CN 11-1777/TP

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Performance Evaluation of Cognitive Radio Networks with Parallel Channels

Chen Xin1, Xu Tong2, Xiang Xudong3, and Wan Jianxiong4   

  1. 1(School of Computer Science, Beijing Information Science & Technology University, Beijing 100101) 2(School of Information Management, Beijing Information Science & Technology University, Beijing 100101) 3(School of Computer & Communication Engineering, University of Science and Technology Beijing, Beijing 100083) 4(College of Information Engineering, Inner Mongolia University of Technology, Huhhot 010051)
  • Online:2013-10-15

Abstract: The cognitive radio (CR) is a newly proposed wireless communication technology to improve the spectrum utilization. Through channel sensing and learning, cognitive radio systems exploit the unused spectrums and occupy it so that the network can allocate the spectrum dynamically and improve the utilization of available spectrum. There are a number of research focusing on the single-channel cognitive radio systems, however the CR system with parallel channels is an unexplored area because of its complexity. In this paper, we conduct performance evaluation of the CR system with parallel channels was conduct by using stochastic network calculus (SNC). The data arrival process for all primary/secondary users and the service process of parallel channels are characterized by Markov modulated deterministic processes (MMDP). Then we formulate the data arrival process and service process as arrival curve and service curve in the SNC theory. The closed form solutions of stochastic performance boundary of the delay and backlog are obtained using the delay bound theorem and backlog theorem in SNC theory. Finally, we make extensive simulations to evaluate the correctness of the proposed model, and conduct performance analysis of the CR system with parallel channels with various parameter settings. The results show that our proposed framework can effectively model and analyze the CR system with parallel channels.

Key words: cognitive radio networks, stochastic network calculus, Markov chain, delay, backlog