Spectrum Sensing Analysis of Wireless Cognitive Radio Sensor Network with Soft-data Fusion Over Generalized $\alpha-\mu$ Fading Channel

This research describes the spectrum sensing capa-bilities of an energy-detection (ED) based wireless cognitive radio sensor network (WCRSN) with soft-data fusion. In particular, a number of network parameters that take into account channel defects, noise, and generalized
$\alpha-\mu$
fading are generated to predict the performance of the WCRSN. For network under consideration, a cognitive radio sensor (CRS) first detects a primary user (PU), collects sensing information, and forwards the data to the control center (CC) without making a local binary choice. At CC, a final determination regarding the status of the PU is made after combining the locally acquired individual soft data using a soft data fusion technique. To do this, initially, the formula for the novel and analytical detection probability in a CRS is created and confirmed using experimental setup. This equation integrates noise and
$\alpha-\mu$
fading. Next, closed-form expressions of a cooperative detection probability in CC subject to soft-data fusion and average error rate (AER) under noisy and
$\alpha-\mu$
fading conditions are derived. Using complementary receiver operating characteristics (CROC) and AER, the significant impact of channel and network parameters is evaluated. Finally, the effects of channel and network parameters on the functionality of the WCRSN are investigated. It has also been investigated how hard-decision and soft-data fusion operate differently.