Optimization of Wireless Spectrum Sensing Algorithm Based on Deep Learning
This paper proposes a wireless spectrum sensing method based on deep learning,and introduces L1 regularization method for optimization.Firstly,the basic principle of wireless spectrum sensing is analyzed.Secondly,a spectrum sensing model based on Recurrent Neural Network(RNN)is designed,and the traditional RNN model is optimized by L1 regularization.Finally,the experimental verification is carried out by using Single Input Multiple Output(SIMO)collaborative spectrum sensing scene data set.The experimental results show that compared with the traditional RNN method,the optimized RNN model achieves better performance in terms of accuracy,recall and F1 value,which proves that the proposed method has significant advantages in improving spectrum sensing performance.
deep learningspectrum sensinglong short-term memory modelL1 regularization