time-series-4

Automatic Modulation Recognition

3260 papers • 126 benchmarks • 313 datasets

Automatic modulation recognition/classification identifies the modulation pattern of communication signals received from wireless or wired networks.

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Most implemented papers

A light neural network for modulation detection under impairments

Thomas Courtat, Hélion Marie du Mas des Bourboux•Thu Mar 26 2020

A neural network architecture able to efficiently detect modulation scheme in a portion of I/Q signals that is lighter by up to two orders of magnitude than other state-of-the-art architectures working on the same or similar tasks and results in a signal-length invariant network.

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A Spatiotemporal Multi-Channel Learning Framework for Automatic Modulation Recognition

Jialang Xu, Chunbo Luo, G. Parr, Yang Luo•Mon Jun 01 2020

Automatic modulation recognition (AMR) plays a vital role in modern communication systems. This letter proposes a novel three-stream deep learning framework to extract the features from individual and combined in-phase/quadrature (I/Q) symbols of the modulated data. The proposed framework integrates one-dimensional (1D) convolutional, two-dimensional (2D) convolutional and long short-term memory (LSTM) layers to extract features more effectively from a time and space perspective. Experiments on the benchmark dataset show the proposed framework has efficient convergence speed and achieves improved recognition accuracy, especially for the signals modulated by higher dimensional schemes such as 16 quadrature amplitude modulation (16-QAM) and 64-QAM.

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An Efficient Deep Learning Model for Automatic Modulation Recognition Based on Parameter Estimation and Transformation

Jialang Xu, Chunbo Luo, Yang Luo, Fu Zhang•Thu Sep 30 2021

This letter proposes an efficient DL-AMR model based on phase parameter estimation and transformation, with convolutional neural network and gated recurrent unit as the feature extraction layers, which can achieve high recognition accuracy equivalent to the existing state-of-the-art models but reduces more than a third of the volume of their parameters.

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

Jialang Xu, Chunbo Luo, Yang Luo, Fu Zhang, Fuchun Zheng•Thu Jun 30 2022

A review of the current DL-AMR research is presented, with a focus on appropriate DL models and benchmark datasets, and comprehensive experiments are provided to compare the state of the art models for single-input-single-output (SISO) systems from both accuracy and complexity perspectives.

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