RBFNN-Based Adaptive Equalization for Enhanced Spectrum Sensing in Cognitive Radio Networks

In the age of limited spectrum, cognitive radios (CRs) are becoming a key tool for effective spectrum use. Accurate spectrum sensing (SS) in the presence of channel impairments such as fading, noise, and interference is a crucial problem for CRs, especially during periods of low Signal-to-Noise Ratio (SNR). Here, we suggest a radial basis function neural network (RBFNN)-based adaptive equalization framework integrated with spectrum sensing techniques, specifically cyclostationary feature detection (CFD) and energy detection (ED), in order to attain a high level of detection accuracy and low false alarm rates. Moreover, the equalizer improves signal quality under nonlinear noisy channel conditions using supervised learning-based optimization. Results from the simulation show improved bit error rate (BER) and mean square error (MSE) performance for SNR ranging from 0 to 25 dB on nonlinear and noisy channels. Compared with the conventional energy detector operating without equalization, the proposed RBFNN-assisted framework achieves an 18.6% increase in detection probability and a 14.3% reduction in false alarm probability at an SNR of 5 dB. Moreover, the proposed scheme also demonstrates superior performance in terms of signal reconstruction and robust spectrum sensing in fading channels.