Threshold selection analysis of spectrum sensing for cognitive radio network with censoring based imperfect reporting channels

Abstract

An appropriate threshold selection scheme is one of the main components to adjudicate the performance of energy detection spectrum sensing (EDSS) technique for cognitive radio network. In this paper, we have employed two different threshold selection approaches namely, the constant false-alarm rate (CFAR) and minimized error probability (MEP) and analyzed the threshold selection effects on the performance of cognitive user (CU) communication systems particularly, the total spectrum sensing error probability and throughput. We have derived the expressions and analyzed these performance parameters by considering an imperfect spectrum sensing and reporting channels in the cooperative spectrum sensing scenarios for additive white Gaussian noise (AWGN), Rayleigh and Nakagami-m fading environments. In addition, the censoring concept has been applied to the proposed system and compared its effect with that of the non-censoring based cognitive radio network (CRN) system under the perfect reporting (PR) and imperfect reporting (IR) channel. With the help of simulation, we have illustrated that the role of threshold selection approach is crucial to maximize the throughput and minimize the spectrum sensing error while considering the amount of error in the reporting channel. Further, from the results, the existence of trade-off between the spectrum sensing error probability and throughput is presented with threshold selection approaches. Moreover, it is also shown that there is need to switch among CFAR and MEP threshold selection approaches in the censoring scenario, to enhance the throughput and decrease the spectrum sensing error probability.