Research On Communication Signal Modulation Recognition Algorithm Based On Deep Learning

Automatic modulation identification of communication signals refers to a process of automatically identify different communication signals.As an intermediate part of signal detection and demodulation,modulation recognition technology plays an important role in a variety of civilian and military applications such as software radio,electronic countermeasure,and intelligent demodulator.After several decades of development,modulation recognition technology has made many achievements.However,with the increasing demand for engineering and the increasingly complex wireless communication channel environment,there are still many issues to be solved.This paper introduces a method of deep learning for communication signal modulation recognition.Deep learning allows for automatically learn features directly from simple wireless signal representations,without the need to design of hand-crafted expert features such as high-order cyclic moments.First of all,this paper introduces the basic theory of deep learning,including fully connected neural networks and convolutional neural networks.The fully connected neural network describes the mathematical model of neurons,the cost function and the backward propagation algorithm.Convolutional neural network describes the mathematical principles of convolution and pooling.Secondly,this paper applies the deep belief network to communication signal modulation recognition by studying the theoretical knowledge of deep belief network in deep learning,this paper studies the communication signal modulation recognition algorithm based on deep belief network.And the algorithm is compared with the traditional machine learning algorithm to verify the effectiveness of the algorithm.The simulation results show that the recognition accuracy of the communication signal modulation recognition algorithm based on the deep belief network is higher than other machine learning algorithms on the premise of increasing the computational complexity.Finally,considering the problem that the recognition accuracy of communication signal modulation recognition algorithm based on deep belief network is not ideal,this paper improves the original algorithm and studies the communication signal modulation recognition algorithm based on convolution deep belief network.The simulation results show that the communication signal modulation recognition algorithm based on the convolution deep belief network has higher recognition accuracy than the original algorithm on the premise of increasing the computational complexity.This paper provides a new idea for the application of deep learning in modulation recognition in the field of communication.