EXPERIMENTAL STUDY OF THE SPECTRUM SENSOR ARCHITECTURE BASED ON DISCRETE WAVELET TRANSFORM AND FEED-FORWARD NEURAL NETWORK

In this paper, we present an experimental study of a new spectrum sensor architecture based on application of discrete wavelet transform for preprocessing and feed forward neural network for classification. For the experimental study, we select three different wavelets: Haar, Daubechies and Symlet. The discrete wavelet transform is applied to radio signal spectral components. The output of wavelet transform we use as an input to the feed-forward neural network (FFNN). The hypothesis on the presence of the primary user signal is made by FFNN with binary output activation function. The proposed spectrum sensor is implemented in FPGA based system and tested on a real environment measures. The spectrum sensing results compared with spectrum sensor based on cyclostationary features. The results of the experimental study shows the ability to use effectively the Haar wavelet in conjunction with FFNN while the amount of not detected primary user emissions remains less than 1.6%. The signal processing is performed in real-time and ads only 52 ns delay.