Improved MB Cognitive Radio Spectrum Sensing Using Wavelet Spectrum Filtering

Abstract

In cognitive radio (CR), the sensed aggregate bandwidth could be as large as several GHz. This is especially challenging if the bandwidths and central frequencies of the sensed signals are unknown and need to be estimated. This work discusses a new improved method for MB spectrum sensing (iMB-SS) based on edge detection and using Wavelet Spectrum Filtering. The proposed iMB-SS method uses a Welch power spectrum density (PSD) estimate and a multi-scale Wavelet approach to reveal the spectrum transition (edges), which is deployed to characterize the spectrum occupancy in CR scenarios where the operation frequencies of the primary users (PUs) are unknown. The focus of this work lies in improving the performance of the MB spectrum sensor, particularly by refining the spectral edge location and reducing misleading detection. A comprehensive analytical description and numerical analysis have been carried out by focusing on orthogonal-frequency-division-multiplexing (OFDM) signal applications in CR networks. Numerical results corroborate the effectiveness of the proposed iMB-SS approach. The simulated results for the multiple-PU’s OFDM-based transmission CR system demonstrate that the proposed iMB-SS method can achieve high performance even under low signal-to-noise ratio (SNR) regime, turning it out as an attractive choice for SS in the MB CR systems.