A Neural Network Spectrum Sensing Algorithm Using Bee Colony Optimization

Neural network is easy to converge on local optimal solution, and its training convergence speed is slow. In addition, the optimal solution has a deep influence on performance of a spectrum sensing algorithm of neural network. Therefore, neural network is cross-trained by bee colony algorithm to accelerate the training convergence speed and to reduce the mean square error so as to improve the performance of a neural network spectrum sensing algorithm. Using signal energy and spectral correlation as feature parameters, a neural network spectrum sensing algorithm using bee colony optimization is proposed. Simulation results show that with a certain number of iterations, the proposed algorithm has better sensing performance compared with the spectrum sensing algorithm based on energy detection, the spectrum sensing algorithm based on cyclostationary, the neural network spectrum sensing algorithm without bee colony cross training and spectrum sensing algorithm based on RBF neural network.