Cooperative Spectrum Sensing over Imperfect Channels

Cognitive radio has emerged as an innovative approach able to cope with the spectral limitations. Cognitive radio networks rely on detecting whether a particular segment of the radio spectrum is currently in use and to exploit the temporarily unused spectrum rapidly without interfering with the transmissions of other users. Thus, one of the most important and critical components of the cognitive radio is spectrum sensing and accordingly, detection of primary users. Recently proposed cooperative spectrum sensing methods do not consider errors occurring during the transmission of local cognitive radio decisions to the cognitive base station. However, perfect communication is clearly not the case in realistic cooperative spectrum sensing scenarios and might lead to misleading performance result interpretations. In this paper, we extend the cooperative spectrum sensing model to admit transmission imperfections and propose two simple, hardware-friendly and effective primary user detectors operating on noisy local cognitive radio decisions. We present simulation results investigating the performance loss incurred by considering realistic channels with varying signal-to-noise ratio values. Moreover, we compare the two proposed cooperative spectrum sensing algorithms in various practical scenarios.