Research On Wideband Spectrum Sensing Method Based On Deep Learning And Undersampling

In the cognitive radio system,wideband spectrum sensing technology plays an important role,and various methods related to it are emerging one after another.Wideband signals carry a wealth of time domain information,and are often sparse in the frequency domain in practical applications.How to effectively extract the time-frequency characteristics of wideband signals is still a difficult point in spectrum reconstruction and perception.In order to improve the performance of wideband spectrum sensing,this paper uses the method of co-prime sampling combined with deep learning ideas to conduct research.The research content includes the following three aspects:First,in order to reconstruct the wideband spectrum and reduce the difficulty of constructing a wideband spectrum sensing model,a deep iterative network based on complex convolution is designed.The objective function is solved by the alternating direction multiplier method,and the algorithm iterative process is expanded into a multi-module,multi-level network structure by the deep expansion method.The iterative operator module composed of one-dimensional convolution and nonlinear mapping layers is used to complete the wideband spectrum.The reconstruction task achieves the purpose of improving the accuracy of spectrum sensing.Secondly,in order to improve the accuracy of spectrum reconstruction and reduce the occupation of storage resources in the network training process,a deep complex valued neural network based on complex convolution is designed.Input the under-sampled complex signal into the deep complex-valued neural network,introduce the residual connection,and post the information between the front and back convolutional layers of the network,and obtain the complex signal spectrum reconstruction result through correlation operation.Through the network,the mapping relationship between the complex signals before and after the under-sampling in the corresponding subbands in the frequency domain is obtained,which improves the accuracy of spectrum reconstruction.This method can reconstruct the wideband signal spectrum after coprime sampling and improve the detection probability of spectrum sensing.Finally,in order to improve the detection performance of the network,a self-attention mechanism and a deep detection network are introduced to construct a wideband spectrum sensing network.The network uses multiple one-dimensional convolution operations and other correlation operations to use the correlation of sampling points in the wideband signal sequence to obtain the weight of each sampling point in the input signal,and input the weighted input signal to the depth detection network for spectrum sensing.This method obtains a faster wideband spectrum spectrum sensing speed,and improves the accuracy of wideband spectrum sensing.