Enhanced Double-Threshold Cooperative Spectrum Sensing for Cognitive Radio Networks

With the rapid expansion of wireless services, efficient spectrum utilization has become a critical challenge. Research indicates that while certain frequency bands experience heavy congestion, others remain largely unused. Cognitive radio (CR) offers a promising solution by enabling Secondary Users (SUS) to opportunistically access idle licensed spectrum through spectrum sensing. Among sensing methods, energy detection is popular for its simplicity, yet its performance declines under low signal-to-noise ratio (SNR) conditions. To overcome this, Cooperative Spectrum Sensing (CSS) and double-threshold techniques have been introduced to enhance detection accuracy and spectrum efficiency while protecting Primary Users (PUs). However, these methods are not immune to sensing failures. This work proposes an improved double-threshold CSS strategy, where each CR forwards local soft decisions-represented as decimal values between and ―to a Fusion Center (FC). The FC employs a soft-combining approach to determine the final outcome. A simulation framework is developed to evaluate the proposed algorithm, and results confirm that it substantially outperforms conventional double-threshold CSS. Specifically, at an SNR of -V dB with a probability of false alarm of, the proposed method achieves nearly a ۱۴% gain in cooperative probability of detection.

Keywords:

Cognitive radio , spectrum sensing , energy detection , double threshold , cooperative spectrum sensing , soft decision