The Use of Rateless Coding Technique in Cognitive Radio Networks Based on Primary User Channel Occupancy Modeling
محورهای موضوعی : Majlesi Journal of Telecommunication DevicesIman Pourmohammadi 1 , H Farrokhi 2
1 - Department of Electrical and Computer Engineering, Birjand University, Iran
2 - Department of Electrical and Computer Engineering, Birjand University, Iran
کلید واژه: en,
چکیده مقاله :
In this paper, we investigate the channel selection technique in secondary user communication in cognitive radio network using rateless codes. In order to increase the tolerance of interference from the primary user appearance, also considering losses caused by collision between several secondary users, each secondary user uses rateless codes. We model the primary user occupancy and interference dynamics of a channel, which is used by a secondary user, using a Hidden Markov Model (HMM). The HMM is trained using Baum-Welch procedure and each secondary user uses a trained HMM to predict the primary user channel occupancy in future time slots and compute the Channel Availability Metric (CAM) for the channel. CAM is used by secondary user to select a preferable primary user channel for its communication. Simulation results, demonstrate the efficiency of the proposed channel selection technique in secondary user communication.
[1] H. Kushwaha, Y. Xing, R. Chandramouli, and H. Heffes, “Reliable multimedia transmission over cognitive radio networks using fountain codes,” Proceedings-IEEE, Vol. 96, No. 1, pp. 155, 2008.
[2] G. YUE and X. WANG, “Anti-Jamming Coding Techniques with Application to Cognitive Radio,” IEEE transactions on wireless communications , Vol. 8, No. 12, pp. 5996–6007, 2009.
[3] D. J. C. MacKay, “Fountain codes,” IEEE Proc.-Commun., Vol. 152, No. 6, pp. 1062–1068, Dec. 2005.
[4] M. Mitzeumacher, “Digital Fountains: A Survey and Look Forward,” IEEE Information Theory Workshop, San Antonio, Texas, October 2004.
[5] M. Luby, “LT codes,” in Proc. 43rd Annu. IEEE Symp. Found. Comput. Sci. (FOCS), Vancouver, BC, Canada, pp. 271–280, Nov. 2002.
[6] A. Shokrollahi, “Raptor codes,” IEEE Trans. Inf. Theory , vol. 52, no. 6, pp. 2551–2567, Jun. 2006.
[7] D. J. C. MacKay, Information Theory, Inference, and Learning Algorithms . Cambridge, U.K.: Cambridge Univ. Press, 2003.
[8] Q. Zhao and B. Sadler, “A survey of dynamic spectrum access: signal processing, networking, and regulatory policy,” IEEE Signal Processing Mag., Vol. 24, No. 3, pp. 79-89, May 2007.
[9] S. Chen, Z Zhang, X. Chen, and K. W, “Distributed spectrum access in cognitive radio network employing rateless codes,” in proc. IEEE Globecom, 2010.
[10] J. Proakis and M. Salehi, Digital communications, 5th ed. Singapore: McGraw Hill, 2008.
[11] C. Ghosh, C Cordeiro, D. P. Agrawal, and M. B. Rao, “Markov chain existence and hidden markov models in spectarum sensing,” in proc. 7th Annu. IEEE Int. Conf. on Perv. Comput. And Commun., Galveston, 2009, pp. 1–6.
[12] Z. Zhi-Jin, Z. Shi-Lian, X. Chun-Yun, and K. Xian-Zheng, “Discrete channel modeling based on genetic algorithm and simulated annealing for training hidden markov model,” Physical Commun., Vol. 16, No. 6, pp. 1619-1623, Jun. 2007.
[13] Lawrence R. Rabiner, “A tutorial on hidden Markov models and selected applications in speech recognition,” in Proc. IEEE, Vol. 77, No. 2, February 1989.
[14] R. Duda, P. Hart, and D. Stork, Pattern classification, 2nd ed. Wiley & Sons, Inc., 2001.
[15] M. Sharma, A. Sahoo, and K. D. Nayak, “Channel modeling based on interference temerature in underlay cognitive wireless networks,” in IEEE International Symposium on Wireless Communication Systems, 2008.
