Prediction‐based MAC‐layer sensing in cognitive radio networks. Issue 2 (26th July 2014)

• Record Type: Journal Article
• Authors: Zhang, Lei; Xie, Cihang; Jiang, Tao; Shen, Junyang; Qi, Qi; Meng, Dexiang
• Is Part Of: Wireless communications and mobile computing. Volume 16:Issue 2(2016)
• Page Start: 128
• Page End: 136
• Publication Date: 2014-07-26
• Subjects: cognitive radio networks -- sensing scheduling -- prediction -- Hungarian algorithm; Wireless communication systems -- Periodicals; Mobile communication systems -- Periodicals; 621.38205
• DOI: 10.1002/wcm.2498
• Languages: English
• ISSNs: 1530-8669

Abstract

Abstract: We consider a cognitive radio network which coexists with multiple primary users (PUs) and secondary users (SUs) transmit over time‐varying channels. In this scenario, one problem of the existing work is the poor performances of throughput and fairness due to variances of SUs' channel conditions and PUs' traffic patterns. To solve this problem, we propose a novel prediction‐based MAC‐layer sensing algorithm. In the proposed algorithm, the SUs' channel quality information and the probability of the licensed channel being idle are predicted. Through the earlier predicted information, we schedule the SUs to sense and transmit on different licensed channels. Specifically, multiple significant factors, including network throughput and fairness, are jointly considered in the proposed algorithm. Then, we formulate the prediction‐based sensing scheduling problem as an optimization problem and solve it with the Hungarian algorithm in polynomial time. Simulation results show that the proposed prediction‐based sensing scheduling algorithm could achieve a good tradeoff between network throughput and fairness among SUs. Copyright © 2014 John Wiley & Sons, Ltd. Abstract : We consider a cognitive radio network which coexists with multiple primary users and secondary users (SUs) transmit over time‐varying channels. A novel prediction‐based MAC‐layer sensing algorithm was proposed in this paper. Simulation results show that the proposed prediction‐based sensing scheduling algorithm could achieve a good tradeoff between network throughput and fairness among SUs.