A Spectrum Sensing Algorithm in Cognitive Radio Based on Improved Quantum Neural Network
Zhao Yan · Radio and communications technology · 2015
To overcome the shortcomings of traditional sensing and enhance performance at low SNR,this paper proposes a sensing algorithm based on improved quantum neural network. The basic idea is to extract characteristic parameters by an authorized user signals and train quantum neural network,then to access authorization data signals uncertainty and store,to achieve ambient spectrum opportunitytest. In order to enhance the convergence and stability of quantum neural network,the quantum neural network is improved. The new algorithm chooses the three-layer Josephson function as transfer function to shorten excitation of the saturation zone and reduce thefalse saturationphenomenon occurred during training; With constraints added in the original learning objectives functions,the interaction of network weights adjustment and updating quantum in during learning process decreases to a minimum. As a result,the experiment results show that the improved quantum neural network has a faster convergent speed and a higher stability compared with the quantum neural network and BP neural network. Also,the improved QNN has a higher detection probability at the low SNR environment.