Wideband spectrum sensing in cognitive radio using discrete wavelet packet transform and principal component analysis

Abstract The cognitive radio is a wireless technology which offers so much promise in the mitigation of the problem of spectrum scarcity. To achieve this noble objective, a cognitive radio must be characterized by speed and accuracy. In this paper, we present a spectrum sensing technique using discrete wavelet packet transform (DWPT) and principal component analysis (PCA) with convex optimization. An input signal was decomposed using DWPT to generate the required data matrix whose dimension was reduced using PCA and optimized using convex optimization. This technique was extensively compared with spectrum sensing using discrete Fourier transform (DFT), which is a popular wideband scheme. It was found that the DWPT scheme significantly outperformed DFT in spectrum detection accuracy. This was attributed to the fact that the time–frequency nature of wavelet transform makes it less lossy, i.e., it preserves information content much more than the DFT. The effect of PCA on the scheme was also evaluated and it was found that data dimensionality reduction was considerable, which impacts positively on the speed of spectrum detection. Data dimensionality reduction due to PCA also translates to memory and energy savings for the CR, since less energy is spent in processing signal data. It can then be said that this technique is also energy efficient, a desirable feature for mobile devices.