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Intelligent Modulation Recognition of communication signal for Next-Generation 6G Networks

Computers, Materials & Continua 2023年第3期74卷 5723-5740页

作者： Mrim M.Alnfiai Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia

In recent years,the need for a fast,efficient and a reliable wireless network has increased *** 5G networks have already been tested while a few are in the early stages of *** noncooperative communication scenarios,the recognition of digital signal modulations assists people in identifying the communication targets and ensures an effective management over *** recent advancements in both Machine Learning(ML)and Deep Learning(DL)models demand the development of effective modulation recognition models with self-learning *** this background,the current research article designs aDeep Learning enabled Intelligent Modulation Recognition of communication signal(DLIMR-CS)technique for next-generation *** aim of the proposed DLIMR-CS technique is to classify different kinds of digitally-modulated *** addition,the fractal feature extraction process is appliedwith the help of the Sevcik Fractal Dimension(SFD)***,the extracted features are fed into the Deep Variational Autoencoder(DVAE)model for the classification of the modulated *** order to improve the classification performance of the DVAE model,the Tunicate Swarm Algorithm(TSA)is used to finetune the hyperparameters involved in DVAE model.A wide range of simulations was conducted to establish the enhanced performance of the proposed DLIMR-CS *** experimental outcomes confirmed the superior recognition rate of the DLIMR-CS model over recent state-of-the-art methods under different evaluation parameters.

关键词： 6G networks communication signal modulation recognition deep learning machine learning parameter optimization

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Modulation recognition of communication signals based on SCHKS-SSVM

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Journal of Systems Engineering and Electronics 2017年第4期28卷 627-633页

作者： Xiaolin Zhang Jian Chen Zhiguo Sun College of Information and communication Engineering Harbin Engineering UniversityHarbin 150001China Science and Technology on Information Transmission and Dissemination in communication Networks Laboratory Shijiazhuang 050000China

A novel modulation recognition algorithm is proposed by introducing a Chen-Harker-Kanzow-Smale (CHKS) smooth function into the C-support vector machine deformation algorithm. A set of seven characteristic parameters is selected from a range of parameters of communication signals including instantaneous amplitude, phase, and frequency. And the Newton-Armijo algorithm is utilized to train the proposed algorithm, namely, smooth CHKS smooth support vector machine (SCHKS-SSVM). Compared with the existing algorithms, the proposed algorithm not only solves the non-differentiable problem of the second order objective function, but also reduces the recognition error. It significantly improves the training speed and also saves a large amount of storage space through large-scale sorting problems. The simulation results show that the recognition rate of the algorithm can batch training. Therefore, the proposed algorithm is suitable for solving the problem of high dimension and its recognition can exceed 95% when the signal-to-noise ratio is no less than 10 dB.

关键词： communication signal modulation recognition support vector machine smooth function

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