Background:
Radio spectrum is natural and the most precious means in wireless
communication systems. Optimal spectrum utilization is a key concern for today's cutting-edge
wireless communication networks. The impending problem of the lack of available spectrum
has prompted the development of a new idea called “Cognitive Radio” (CR). Cooperative
spectrum sensing (CSS) is utilized to improve the detection performance of the system. Several
fusion algorithms of decision-making are proposed for sensing the licensed user, but they do
not work well under low signal-to-noise ratio (SNR).
Objectives:
To address the issue of poor detection performance under low SNR, Empirical
mode decomposition (EMD) and artificial neural network (ANN) based CSS under Rayleigh
multipath fading channel in IEEE 802.22 wireless regional area network (WRAN) is proposed
in this paper.
Method:
In this work, we propose the use of ANN as a fusion center. First, the received signal's
energy is calculated using EMD. The computed energy, SNR, and false alarm probability
are combined to form a data set of 2048 samples. They are utilized to train Levenberg-
Marquardt back propagation training algorithm-based feed-forward neural network (FFNN).
Using this trained network, CSS in WRAN is simulated under Rayleigh multipath fading.
Results:
Simulation results show that the proposed CSS method based on EMD-ANN outperforms
the standard fast Fourier transform (FFT) and EMD detection-based cooperative spectrum
sensing with a hard "OR" fusion at low SNR. With Pf =0.01, 100% detection accuracy
with proposed techniques is obtained at SNR= -22dB.
Conclusion::
The findings show that the suggested approach outperforms EMD and FFT based
energy detection scheme-based traditional CSS in low SNR environments.