Signal Processing-Based Deep Learning for Blind Symbol Decoding and Modulation Classification

Hanna, Samer; Dick, Chris; Čabrić, Danijela

doi:10.1109/jsac.2021.3126088

Cited by 35 publications

(19 citation statements)

References 38 publications

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“…), could lead to a potential breakthrough in AMR [93]. Furthermore, signal processing techniques such as filtering [16,85] and denoising [88] can also help retain distinctive data features and improve system performance.…”

Section: Model Optimization and Visualisationmentioning

confidence: 99%

Deep learning based automatic modulation recognition: Models, datasets, and challenges

Zhang

Luo

et al. 2022

Digital Signal Processing

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Section: Model Optimization and Visualisationmentioning

confidence: 99%

Deep learning based automatic modulation recognition: Models, datasets, and challenges

Zhang

Luo

et al. 2022

Digital Signal Processing

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“…The existing wireless communication environment is a challenging one, whereas the electromagnetic signal space is even more complicated. On the other hand, the broadcasted data is huge in size with rapidly-changing signals [15]. Thus, the research investigations conducted upon the automatic and maximum speed modulation pattern detection of multi-class communication modulation signals are extremely valued.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Modulation Recognition of Communication Signal for Next-Generation 6G Networks

Alnfiai¹

2023

Computers, Materials &Amp; Continua

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In recent years, the need for a fast, efficient and a reliable wireless network has increased dramatically. Numerous 5G networks have already been tested while a few are in the early stages of deployment. In noncooperative communication scenarios, the recognition of digital signal modulations assists people in identifying the communication targets and ensures an effective management over them. The recent advancements in both Machine Learning (ML) and Deep Learning (DL) models demand the development of effective modulation recognition models with self-learning capability. In this background, the current research article designs a Deep Learning enabled Intelligent Modulation Recognition of Communication Signal (DLIMR-CS) technique for next-generation networks. The aim of the proposed DLIMR-CS technique is to classify different kinds of digitally-modulated signals. In addition, the fractal feature extraction process is applied with the help of the Sevcik Fractal Dimension (SFD) approach. Then, the extracted features are fed into the Deep Variational Autoencoder (DVAE) model for the classification of the modulated signals. In 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 model. The experimental outcomes confirmed the superior recognition rate of the DLIMR-CS model over recent state-of-the-art methods under different evaluation parameters.

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“…This paper proposes a multi-data source deep learning Object Detection network (MS-Yolo) based on millimeter wave radar and vision fusion. The authors of [ 23 , 24 , 25 , 26 , 27 ] used different deep learning models and preprocessing algorithms to classify and recognize radar signals, ships, and intracellular molecules. In [ 28 , 29 , 30 ], generative adversarial network and adaptive noise suppression are used to sort and identify modulation features for radar signals, and good results are obtained.…”

Section: Introductionmentioning

confidence: 99%

Low SNR Multi-Emitter Signal Sorting and Recognition Method Based on Low-Order Cyclic Statistics CWD Time-Frequency Images and the YOLOv5 Deep Learning Model

Huang

Yan

Hao

et al. 2022

Sensors

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It is difficult for traditional signal-recognition methods to effectively classify and identify multiple emitter signals in a low SNR environment. This paper proposes a multi-emitter signal-feature-sorting and recognition method based on low-order cyclic statistics CWD time-frequency images and the YOLOv5 deep network model, which can quickly dissociate, label, and sort the multi-emitter signal features in the time-frequency domain under a low SNR environment. First, the denoised signal is extracted based on the low-order cyclic statistics of the typical modulation types of radiation source signals. Second, the time-frequency graph of multisource signals was obtained through CWD time-frequency analysis. The cyclic frequency was controlled to balance the noise suppression effect and operation time to achieve noise suppression of multisource signals at a low SNR. Finally, the YOLOv5s deep network model is used as a classifier to sort and identify the received signals from multiple radiation sources. The method proposed in this paper has high real-time performance. It can identify the radiation source signals of different modulation types with high accuracy under the condition of a low SNR.

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Signal Processing-Based Deep Learning for Blind Symbol Decoding and Modulation Classification

Cited by 35 publications

References 38 publications

Deep learning based automatic modulation recognition: Models, datasets, and challenges

Deep learning based automatic modulation recognition: Models, datasets, and challenges

Intelligent Modulation Recognition of Communication Signal for Next-Generation 6G Networks

Low SNR Multi-Emitter Signal Sorting and Recognition Method Based on Low-Order Cyclic Statistics CWD Time-Frequency Images and the YOLOv5 Deep Learning Model

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