Reliable and low-complexity wavelet-based spectrum sensing for cognitive radio systems at low SNR regimes

A low-complexity spectrum sensing technique based on wavelet transform is proposed to improve the sensing reliability at low signal-to-noise ratio (SNR) regimes. In this scheme, the received signal is discretely sampled and decomposed into multi-level detail and approximation coefficients where the distribution of the coefficients is used as the decision metric (DM) to distinguish primary signals from spectrum holes. By using this novel DM, it is shown that the proposed scheme significantly improves the detection performance in terms of complexity and reliability, particularly at low SNR regimes.