Research On Signal Recognition Model Lightweight Method Based On Deep Learning

Signal recognition is an important sub field in the field of information science.The development of modern electronic information system,such as communication and radar,is inseparable from the development of signal processing technology.With the continuous progress of science and the rapid development of communication technology,various kinds of communication electronic equipment are widely used.Deep learning is one of the most popular branches in the field of artificial intelligence.It has achieved better results than traditional methods in many application scenarios.In recent years,more and more deep convolution neural networks are used in the field of signal recognition.Although deep learning has many advantages that traditional methods can't compare with,but the large and deep deep network model memory and computation are very large,and it can't be applied to embedded devices and some small mobile devices.Therefore,under the premise of reasonable performance loss of convolutional neural network,model lightweight is a very important research direction.The main work of this paper is as follows:(1)The convolution neural network structure for signal recognition is analyzed.The convolution of convolution layer in convolution neural network is changed into deep separable convolution.In this method,the general convolution is divided into two parts,the first part is the depth convolution,the second part is the point convolution.Through the combination of the two parts,the compression of the network is achieved.This method can effectively reduce the size of the model and accelerate the forward reasoning speed of the model.(2)Aiming at the problem that the convolution kernel redundancy in convolution neural network convolution layer of signal recognition results in too large model and too much forward reasoning time,this paper proposes an interval based convolution kernel pruning method.By extracting the weight parameters of the convolution layer of the trained model,the weight interval of the convolution kernel is divided into several equal intervals,and the pruning coefficient of each interval is set to prune the convolution kernel of the convolution layer,and then the model is retrained.Compared with the traditional method,this method can achieve a larger compression ratio when the same accuracy is lost.(3)In this paper,singular value decomposition(SVD)is used to reduce the weight of all connection layer in convolutional neural network.SVD can reduce the number of matrix calculation by decomposing a large matrix into several small multiplication forms,and achieve the goal of model lightweight when the model performance loss is acceptable.(4)For different types of signal recognition convolution neural network,reasonable use of the above methods for combination,in different data sets for detailed experimental comparison,in the case of acceptable model performance loss to achieve better results than a separate lightweight algorithm.