Spectrum Behavior Prediction and Optimized Throughput /Time performance Using FFNN in Cognitive Radio

Abstract

Progressively, number of radio spectrum users is increasing as life tends towards new technologies in all sectors, so even those users of licensed band are demanding larger radio spectrum. Users may get assigned into other bands to balance the radio spectrum congestion. In this paper, radio spectrum is sensed for void detection and secondary user assignment. Cognitive users are participating the white band either by transmitting alongside with primary users or waiting until the hole is getting vacant. During the period of transmission, the behaviors of primary users are studied for determining the spectrum occupancy status. The activity of primary users is simulated as random variables due to uncertain behaviors from time perspectives. Issues like channel noise and fading effects stand as interrupters of spectrum sensing which make spectrum holes to appear busy due to such incidents. Cognitive Radio network is modeled by using MATLAB software so that both primary and secondary users can sense the spectrum and share the spectrum effectively by employing the approach of waiting time estimator which provides behaviors and activity matrix. Candidates are made to share the spectrum and hereafter transmission delay and throughput are examined when underlay and interweave spectrum sharing were in use. Three techniques are used to share the spectrum which are underlay, interweave and Feed Forward Neural Network. The results shown that feed forward neural network is outperformed in both time delay minimization and throughput enhancement.