Impact of mobility prediction on the performance of cognitive radio networks

Authors:
İsmail Bütün;A. Çğatay Talay;D. Turgay Altilar;Murad Khalid;Ravi Sankar
Affiliations:
Department of Electrical Engineering, University of South Florida, Tampa, FL;Department of Computer Engineering, İstanbul Technical University, İstanbul, Türkiye;Department of Computer Engineering, İstanbul Technical University, İstanbul, Türkiye;Department of Electrical Engineering, University of South Florida, Tampa, FL;Department of Electrical Engineering, University of South Florida, Tampa, FL
Venue:
WTS'10 Proceedings of the 9th conference on Wireless telecommunications symposium
Year:
2010

Citing 5
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Abstract

Wireless technology has enabled the development of increasingly diverse applications and devices resulting in an exponential growth in usage and services. These advancements made the radio frequency spectrum a scarce resource, and consequently, its efficient use is of the ultimate importance. To cope with the growing demand, network design focused on increasing the spectral efficiency by making use of advancement in Cognitive Radio technology. Cognitive Radio can reduce the spectrum shortage problem by enabling unlicensed users equipped with Cognitive Radios to reuse and share the licensed spectrum bands. Using the fact that a Cognitive Radio is capable of sensing the environmental conditions and automatically adapting its operating parameters in order to enhance network performance, we would like to make use of its knowledge to predict the mobility of Cognitive Radio users to improve the overall performance of the Cognitive Radio network. This study makes novel use of mobility prediction techniques to enhance reliability, bandwidth efficiency and scalability of the cognitive radio networks. Firstly, prediction techniques are evaluated and compared for prediction accuracy. Secondly, routing protocol reliability, efficiency and scalability performances are evaluated under different prediction techniques. Simulation results verify the performance improvements even with moderate accuracy predictors. Results clearly show that hybrid Markov CDF prediction performs the best. When compared with no prediction it significantly improves average reliability and efficiency by 11% and 8%, respectively.