Performance Analysis of Linear Cooperative Cyclostationary Spectrum Sensing Over Nakagami- $m$ Fading Channels

This paper deals with performance evaluation of linear combination-based cooperative cyclostationary spectrum sensing (CCSS) when both sensing and reporting links are characterized as independent (and not necessarily identically distributed) Nakagami- $m$ fading channels. To do so, we propose a soft-decision fusion rule for performing CCSS in cognitive radio networks. In essence, we formulate the false alarm (FA) probability of a cooperative detector over Nakagami channels. Based on the Padé approximation method, we derive new approximated closed-form expressions for the FA probability. Furthermore, a more accurate expression for the special case of Rayleigh fading is derived. Through numerical simulations, it is shown that the proposed CCSS scheme significantly enhances the secondary network performance in terms of global detection probability metric, as compared with the commonly used equal-gain combining (EGC) method. In addition, we show that the employed cyclostationary detector provides the highest detection probability compared with the other alternatives.