Optimized Spectrum Sensing Algorithm for Cognitive Radio

Abstract

The electromagnetic spectrum is a meager resource of nature. The current standing spectrum allocation policy is unable to put up the demands of wireless communication. This leads to self-motivated spectrum allocation policy. Cognitive radio (CR) technology is a radiant way to increase spectrum utilization by identifying unused and under-utilized spectrum in vigorously changing environments. Spectrum sensing is a one of the input technique of cognitive radio which detects the existence of primary user in licensed frequency band using self-motivated spectrum allocation policies to use unoccupied spectrum. Spectrum sensing is generally based on energy detection and cyclostationary feature detection. Energy detection is a basic spectrum sensing technique but becomes bleak at a low signal-to-interference-and-noise ratio. The fundamental cyclostationary feature detection based on cyclic spectrum estimation can actively detect feeble signals from primary users with a cost of maximum complexity on implementation. The objective of this work is to implement precious spectrum-sensing method in field programmable gate array with pragmatic complexity for CR. Particularly, The proposed new spectrum-sensing method called the adaptive absolute-self-coherent-restoral algorithm has been introduced. The complexity of the consequential algorithm is better than the prior self-coherent-restoral (SCORE) algorithm, such as adaptive least-SCORE (ALS), adaptive cross-SCORE (ACS). Their performance for spectrum sensing is analytically appraised and compared in detail.