Modulation recognition network of multi-scale analysis with deep threshold noise elimination  

作　　者：Xiang LI Yibing LI Chunrui TANG Yingsong LI 

机构地区：[1]College of Information and Communication Engineering,Harbin Engineering University,Harbin 150001,China [2]Key Laboratory of Advanced Marine Communication and Information Technology,Ministry of Industry and Information Technology,Harbin Engineering University,Harbin 150001,China [3]China Coal Technology Engineering Group Chongqing Research Institute,Chongqing 400037,China [4]State Key Lab of Methane Disaster Monitoring&Emergency Technology,Chongqing 400039,China

出　　处：《Frontiers of Information Technology & Electronic Engineering》2023年第5期742-758,共17页信息与电子工程前沿（英文版）

基　　金：Project supported by the National Key R&D Program of China(No.2020YFF01015000ZL);the Fundamental Research Funds for the Central Universities,China(No.3072022CF0806)。

摘　　要：To improve the accuracy of modulated signal recognition in variable environments and reduce the impact of factors such as lack of prior knowledge on recognition results,researchers have gradually adopted deep learning techniques to replace traditional modulated signal processing techniques.To address the problem of low recognition accuracy of the modulated signal at low signal-to-noise ratios,we have designed a novel modulation recognition network of multi-scale analysis with deep threshold noise elimination to recognize the actually collected modulated signals under a symmetric cross-entropy function of label smoothing.The network consists of a denoising encoder with deep adaptive threshold learning and a decoder with multi-scale feature fusion.The two modules are skip-connected to work together to improve the robustness of the overall network.Experimental results show that this method has better recognition accuracy at low signal-to-noise ratios than previous methods.The network demonstrates a flexible self-learning capability for different noise thresholds and the effectiveness of the designed feature fusion module in multi-scale feature acquisition for various modulation types.

关 键 词：Signal noise elimination Deep adaptive threshold learning network Multi-scale feature fusion Modulation ecognition 

分 类 号：TN911.3[电子电信—通信与信息系统] TP18[电子电信—信息与通信工程]