Modulation Classification Based on Extensible Neural Networks

Yang, Guan Qing

doi:10.1155/2017/6416019

Cited by 16 publications

(4 citation statements)

References 20 publications

(49 reference statements)

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“…Oversampling Rate 1.5 SNR Range −10 dB∼20 dB 1 N I/Q denotes the complex baseband I/Q data points per path. 2 N U is number of users, and N R is number of resources.…”

Section: Dataset and Parametersmentioning

confidence: 99%

“…Automatic Modulation Recognition (AMR) analyzes non-cooperative received signals to obtain their modulation types through a series of processes, including signal preprocessing, feature extraction, and classification recognition [1]. AMR has been widely used in various fields, such as cognitive defined radio, military intelligence, communication jammers, surveillance, spectrum management, and communication reconnaissance [2], which can improve spectrum utilization and solve the problem of spectrum shortage. At present, the main goal of the AMR is to quickly and accurately identify the modulation type of the signal for demodulation and analysis, which is an important process for accurately learning and reliably sharing the spectrum to improve the efficiency of spectrum utilization [3].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Deep Learning-Aided Modulation Recognition for Non-Orthogonal Signals

Fan

Wu²,

Zhang³

et al. 2023

Sensors

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Automatic Modulation Recognition (AMR) can obtain the modulation mode of the received signal for subsequent processing without the assistance of the transmitter. Although the existing AMR methods have been mature for the orthogonal signals, these methods face challenges when deployed in non-orthogonal transmission systems due to the superimposed signals. In this paper, we aim to develop efficient AMR methods for both downlink and uplink non-orthogonal transmission signals using deep learning-based data-driven classification methodology. Specifically, for downlink non-orthogonal signals, we propose a Bi-directional Long Short-Term Memory (BiLSTM)-based AMR method that exploits long-term data dependence to automatically learn irregular signal constellation shapes. Transfer learning is further incorporated to improve recognition accuracy and robustness under varying transmission conditions. For uplink non-orthogonal signals, the combinatorial number of classification types explodes exponentially with the number of signal layers, which becomes the major obstacle to AMR. We develop a spatio-temporal fusion network based on the attention mechanism to efficiently extract spatio-temporal features, and network details are optimized according to the superposition characteristics of non-orthogonal signals. Experiments show that the proposed deep learning-based methods outperform their conventional counterparts in both downlink and uplink non-orthogonal systems. In a typical uplink scenario with three non-orthogonal signal layers, the recognition accuracy can approach 96.6% in the Gaussian channel, which is 19% higher than the vanilla Convolution Neural Network.

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“…Oversampling Rate 1.5 SNR Range −10 dB∼20 dB 1 N I/Q denotes the complex baseband I/Q data points per path. 2 N U is number of users, and N R is number of resources.…”

Section: Dataset and Parametersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep Learning-Aided Modulation Recognition for Non-Orthogonal Signals

Fan

Wu²,

Zhang³

et al. 2023

Sensors

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“…Instead of using CNN, authors in [26] proposed a DL architecture based on ENN for AMR in multipath fading channels. The proposed DNN is based on the natural logarithmic energy model.…”

Section: Amrmentioning

confidence: 99%

A Methodology to Design Quantized Deep Neural Networks for Automatic Modulation Recognition

et al. 2022

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Next-generation communication systems will face new challenges related to efficiently managing the available resources, such as the radio spectrum. DL is one of the optimization approaches to address and solve these challenges. However, there is a gap between research and industry. Most AI models that solve communication problems cannot be implemented in current communication devices due to their high computational capacity requirements. New approaches seek to reduce the size of DL models through quantization techniques, changing the traditional method of operations from a 32 (or 64) floating-point representation to a fixed point (usually small) one. However, there is no analytical method to determine the level of quantification that can be used to obtain the best trade-off between the reduction of computational costs and an acceptable accuracy in a specific problem. In this work, we propose an analysis methodology to determine the degree of quantization in a DNN model to solve the problem of AMR in a radio system. We use the Brevitas framework to build and analyze different quantized variants of the DL architecture VGG10 adapted to the AMR problem. The evaluation of the computational cost is performed with the FINN framework of Xilinx Research Labs to obtain the computational inference cost. The proposed design methodology allows us to obtain the combination of quantization bits per layer that provides an optimal trade-off between the model performance (i.e., accuracy) and the model complexity (i.e., size) according to a set of weights associated with each optimization objective. For example, using the proposed methodology, we found a model architecture that reduced 75.8% of the model size compared to the non-quantized baseline model, with a performance degradation of only 0.06%.

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“…Guan et al [29] concentrated on utilizing an extensible neural network (ENN) to classify the modulation scheme. The utilized ENN algorithm extracted features, such as amplitude, frequency, and phase, by using its nonlinear function.…”

Section: Amc With MLmentioning

confidence: 99%

AMC2N: Automatic Modulation Classification Using Feature Clustering-Based Two-Lane Capsule Networks

et al. 2021

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The automatic modulation classification (AMC) of a detected signal has gained considerable prominence in recent years owing to its numerous facilities. Numerous studies have focused on feature-based AMC. However, improving accuracy under low signal-to-noise ratio (SNR) rates is a serious issue in AMC. Moreover, research on the enhancement of AMC performance under low and high SNR rates is limited. Motivated by these issues, this study proposes AMC using a feature clustering-based two-lane capsule network (AMC2N). In the AMC2N, accuracy of the MC process is improved by designing a new two-layer capsule network (TL-CapsNet), and classification time is reduced by introducing a new feature clustering approach in the TL-CapsNet. Firstly, the AMC2N executes blind equalization, sampling, and quantization in trilevel preprocessing. Blind equalization is executed using a binary constant modulus algorithm to avoid intersymbol interference. To extract features from the preprocessed signal and classify signals accurately, the AMC2N employs the TL-CapsNet, in which individual lanes are incorporated to process the real and imaginary parts of the signal. In addition, it is robust to SNR variations, that is, low and high SNR rates. The TL-CapsNet extracts features from the real and imaginary parts of the given signal, which are then clustered based on feature similarity. For feature extraction and clustering, the dynamic routing procedure of the TL-CapsNet is adopted. Finally, classification is performed in the SoftMax layer of the TL-CapsNet. This study proves that the AMC2N outperforms existing methods, particularly, convolutional neural network(CNN), Robust-CNN (R-CNN), curriculum learning(CL), and Local Binary Pattern (LBP), in terms of accuracy, precision, recall, F-score, and computation time. All metrics are validated in two scenarios, and the proposed method shows promising results in both.

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Modulation Classification Based on Extensible Neural Networks

Cited by 16 publications

References 20 publications

Deep Learning-Aided Modulation Recognition for Non-Orthogonal Signals

Deep Learning-Aided Modulation Recognition for Non-Orthogonal Signals

A Methodology to Design Quantized Deep Neural Networks for Automatic Modulation Recognition

AMC2N: Automatic Modulation Classification Using Feature Clustering-Based Two-Lane Capsule Networks

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