[Retracted] An Ensemble Deep Learning Model for Automatic Modulation Classification in 5G and Beyond IoT Networks

Roy, Chirag; Yadav, Satyendra Singh; Pal, Vipin; Singh, Mangal; Patra, Sarat Kumar; Sinha, G. R.

doi:10.1155/2021/5047355

Cited by 10 publications

(8 citation statements)

References 22 publications

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“…3, in which the main errors are that of 8PSK missclassified mainly as QPSK. This is a common error present in previous works [5], [8], [9] and it can be explained due to the similarity between the constellation points between these modulations.…”

Section: A Neural Network With Two Hidden Layersmentioning

confidence: 83%

“…In recent years, new works have aimed to generate small architectures that can be suitable for IoT devices that do not have the capability to process large numbers of parameters. In [5] the authors develop a novel deep learning classifier for IoT-based systems that combines the feature extraction capabilities from two-dimentional information of CNNs and the capability to extract sequential correlations within time series data of the LSTMs and the gated recurrent unit (GRU) based RNN. The model outperformes other preexisting models [6], [7] but without demanding a larger training and computational requirement.…”

Section: Related Workmentioning

confidence: 99%

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Automatic Modulation Classification for low-power IoT applications

Mondino-Llermanos,

Corral-Briones

2024

IEEE Latin Am. Trans.

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The Internet of Things (IoT) has swiftly become one of the most important technologies in recent years. Radio spectrum access represents a stern challenge for the IoT as a consequence of the increased use of connected devices. This is particularly true for IoT devices operating in the unlicensed band where the huge demand for wireless connectivity will require techniques that use the spectrum efficiently. Avoiding training sequences enables a more efficient spectrum usage and has the additional advantage of reducing the power consumption of IoT devices, but it requires modulation identification mechanisms. This paper presents a simple yet efficient method to classify received signals according to their modulation type. We propose the application of a single hidden layer neural network with a small number of trainable parameters for performing the classification between seven different modulation types. The designed classifier achieves a maximum accuracy of 95% when the signal-to-noise ratio (SNR) of the input data is 12 dB, and in the presence of multi-path fading, sample rate offset and carrier frequency offset.

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Section: A Neural Network With Two Hidden Layersmentioning

confidence: 83%

Section: Related Workmentioning

confidence: 99%

Automatic Modulation Classification for low-power IoT applications

Mondino-Llermanos,

Corral-Briones

2024

IEEE Latin Am. Trans.

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“…Because ensemble models can handle the complex and dynamic character of communication signals, they have gained interest in the context of Automatic Modulation Classification (AMC). Ensemble models incorporate information from several sources, which allows them to capture complex patterns present in different forms of modulation and different SNR situations [11], [12]. Several benefits are provided by ensemble models in the context of AMC.…”

Section: Ensemble Learning For Amcmentioning

confidence: 99%

Ensemble Learning Approach for Digital Communication Modulation’s Classification

Benremdane,

Jamal,

Taheri

et al. 2024

IJAIA

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This work uses artificial intelligence to create an automatic solution for the modulation's classification of various radio signals. This project is a component of a lengthy communications intelligence process that aims to find an automated method for demodulating, decoding, and deciphering communication signals. As a result, the work we did involved selecting the database required for supervised deep learning, assessing the performance of current methods on unprocessed communication signals, and suggesting a deep learning network-based method that would enable the classification of modulation types with the best possible ratio between computation time and accuracy. In order to use the current automatic classification models as a guide, we first conducted study on them. As a result, we suggested an ensemble learning strategy based on Transformer Neural Network and adjusted ResNet that takes into account the difficulty of forecasting in low Signal Noise Ratio (SNR) scenarios while also being effective at extracting multiscale characteristics from the raw I/Q sequence data. Ultimately, we produced an architecture for communication signals that is simple to work with and implement. With an accuracy of up to 95%, this solution's optimum and sturdy architecture decides the type of modulation on its own.

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“…The application of ensemble models in Automatic Modulation Classification (AMC) has garnered attention due to its ability to address the complex and dynamic nature of communication signals. Ensemble models integrate diverse sources of information, enabling them to capture intricate patterns inherent in modulation types and varying SNR conditions [11], [12]. Ensemble models offer several advantages in the context of AMC.…”

Section: Ensemble Learning For Amcmentioning

confidence: 99%

Communication Signals Modulations Classification based on Neural Network Algorithms

Benremdane,

Jamal,

Taheri

et al. 2023

Soft Computing, Artificial Intelligence and Applications

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This paper aims to find an automatic solution for the modulation’s classification of different types of radio signals by relying on Artificial Intelligence. This project is part of a long process of Communications Intelligence looking for an automatic solution to demodulate, decode and decipher communication signals. Our work therefore consisted in the choice of the database needed for supervised deep learning, the evaluation of existing techniques on raw communication signals, and the proposal of a solution based on deep learning networks allowing to classify the types of modulation with an optimal ratio (computation time / accuracy). We first carried out a research work on the existing models of automatic classification in order to use them as a reference. We consequently proposed an ensemble learning approach based on tuned ResNet and Transformer Neural Network that is efficient at extracting multi- scale features from the raw I/Q sequence data and also considers the challenge of predicting in low Signal Noise Ratio (SNR) conditions. In the end, we delivered an architecture that is easy to handle and apply to communication signals. This solution has an optimal and robust architecture that automatically determines the type of modulation with an accuracy up to 95%

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[Retracted] An Ensemble Deep Learning Model for Automatic Modulation Classification in 5G and Beyond IoT Networks

Cited by 10 publications

References 22 publications

Automatic Modulation Classification for low-power IoT applications

Automatic Modulation Classification for low-power IoT applications

Ensemble Learning Approach for Digital Communication Modulation’s Classification

Communication Signals Modulations Classification based on Neural Network Algorithms

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