Artificial Neural Network Based Spectrum Sensing in Wireless Regional Area Network

In recent development and improvement of wireless communication system, the cognitive radio (CR) is a potential approach to utilize spectrum efficiently. Spectrum sensing technique arguably is the most significant component of cognitive radio. Cooperative spectrum sensing (CSS) is utilized to improve the detection performance of the system. Several fusion strategies of decision making are presented for sensing the primary user but they do not perform well under low signal to noise ratio (SNR) conditions. This paper proposes artificial neural network (ANN) based CSS under Rayleigh multipath fading channel in IEEE 802.22 wireless regional area network (WRAN). We implemented an ANN in the fusion centre. First, the energy of the received signal is calculated using discrete wavelet packet transform (DWPT). Then, calculated energy, SNR and false alarm probability are used jointly to make a data set of 2048 samples and are used to train Levenberg-Marquardt back propagation training algorithm-based feed-forward neural network (FFNN). Using this trained neural network, CSS in WRAN is simulated under Rayleigh multipath fading channel and results demonstrate the superiority of the proposed approach over traditional CSS with DWPT and Fast fourier transform (FFT) based energy detection schemes in low SNR environment.