An improved compressed wideband spectrum sensing technique based on stationary wavelet transform in Cognitive Radio systems

In this paper, we introduce the Stationary Wavelet Transform (SWT) as an improved method of spectrum sensing in wideband systems. Such systems employing Cognitive Radio require a fast and efficient spectrum sensing technique to locate the edges of the occupied channels and determine spectral holes to enable secondary users to access the spectrum without interfering with existing primary users, and with the increasing need for high data rates, compressive sensing is utilized in cognitive radios allowing us to work with sub-Nyquist rates. The Wavelet Transform has been widely used in many applications as a tool for edge detection. Our research shows how the SWT provides better estimates for the location of channel edges in a wideband system and we support these results with simulations. We also present the SWT in a compressive sensing framework.