Performance analysis of energy-efficient wireless cognitive radio sensor networks in shadow fading environments

Cognitive radio (CR) enables dynamic spectrum access, addressing spectrum scarcity. When integrated with wireless sensor networks (WSNs), the resulting system is known as a wireless cognitive radio sensor network (WCRSN). This study investigates the energy-efficiency and throughput of WCRSNs under sensing (S) channels affected by noise and Log-normal shadowing (LNS) fading. Each cognitive radio sensor (CRS) receives an unknown licensed signal from the primary user (PU) and uses energy detection (ED) to make a local one-bit binary decision. These decisions are sent via control (C) channels to a control center (CC), where a hard-decision combining technique (HDCT) determines the PU’s activity state. A novel mathematical expression for detection probability is derived, accounting for noise and LNS fading. This expression is validated through Monte Carlo Simulations. Analytical models are then developed to evaluate energy-efficiency and throughput under varying network and channel conditions. The effects of erroneous S and C-channels (with channel error probability q) are analyzed. Key parameters such as number of CRSs (K), detection threshold ( \(\lambda \) ), average SNR ( \(\bar{\gamma }\) ), and LNS fading parameter ( \(\sigma \) ) are examined. The study achieves up to 92.54% energy-efficiency and optimized throughput across various scenarios.