Intelligent selective spectrum access in cognitive radio networks

Abstract

Wireless communication technology had enabled numerous services that had placed the current radio frequency bands at scarce. Thus, with the emerging of cognitive radio paradigm, the next generation network is determined to increase spectral efficiency in order to satisfy the wireless services demand. In this study, we proposed a novel solution that optimized the performance of cognitive radio networks that is mainly affected by spectrum sensing and channel selection process. Traditionally, secondary users randomly sense the channels until an idle channel is detected whenever their transmission is being interrupted. Inaccurate detection of spectrum availability had caused delays, throughput degradation and high spectrum handoffs in cognitive radio network. Consequently, this study proposes a novel use of Hidden Markov model for recognition of primary user traffic patterns. Next, prediction of an idle channel selection scheme is being proposed. The main concerned of this scheme is to prevent unnecessary delays in finding spectrum opportunities, reduce the number of primary user collision and number of handoffs. Hence, efficient and successful utilization of unexploited radio resources can be realized. Simulation results clearly verify that by using this proposed solution, the performance of cognitive radio network can be improved in terms of successful channel utilization, primary user collision, delays and number of handoffs.