Two-branch wavelet denoising for accurate spectrum sensing in cognitive radios

In order to detect the unused spectrum bands (the spectrum holes) efficiently in cognitive radios with low signal-to-noise radio (SNR), we propose to adopt two independent branches of wavelet to detect the singularities of the received signals’ power spectrum density (PSD). The sensing structure is flexible such that we can use one or two branches to cope with different SNRs. Under low SNR condition, each branch uses distinct characteristics between noise and signals in the wavelet transform to eliminate the singularities generated by the noise. By using bandpass filter to calculate PSD values of the subbands which are distinguished by the signal’s singularities, the subband with the minimum PSD value among all of the subbands could be found. Then, the results of the two branches are merged and analyzed in order to make the final decision. Finally, we use signal reconstruction to further remove the noise and then accurately detect the spectrum holes. When the SNR is high, only one branch through the denoising procedure is needed to get accurate sensing result. Our simulation results show that the two-branch wavelet method is more accurate than conventional approaches under given SNRs.