Effect of the SNR Model in the Performance Assessment of Cooperative Spectrum Sensing

This work investigates the impact of different signal-to-noise ratio (SNR) models on the performance assessment of spectrum sensing algorithms under varying signal and noise levels. Through analytical derivation and simulations, we demonstrate that the choice of SNR model can significantly influence perceived detection performance, particularly in environments with random noise power. Our results highlight that using an inadequate or poorly described SNR model can lead to inaccurate conclusions. These findings emphasize the critical importance of both selecting an appropriate SNR model and clearly stating it when evaluating spectrum sensing systems.