ConvLSTMAE: A Spatiotemporal Parallel Autoencoders for Automatic Modulation Classification

Shi, Yunhao; Xu, Hua; Jiang, Lei; Qi, Zhang

doi:10.1109/lcomm.2022.3179003

Cited by 16 publications

(4 citation statements)

References 18 publications

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“…Comparing to SOTA CSR methods. In addition, as reported in [6], [37], [38], the accuracy of CSR of the SOTA methods in RADIOML 2016.1, Sig-2019 and RADIOML 2018.01A are 94.51%, 98.51% and 99.4%. Our proposed algorithm also achieves similar accuracy at 93.63%, 98.31%, and 99.1%, which demonstrates that our algorithm can improve OSR performance while ensuring the accuracy of CSR.…”

Section: Comparison Of Auroc and Oscr Among Osr Baselinessupporting

confidence: 53%

The Importance of Expert Knowledge for Automatic Modulation Open Set Recognition

Li,

Wen,

Long

et al. 2023

IEEE Trans. Pattern Anal. Mach. Intell.

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Automatic modulation classification (AMC) is an important technology for the monitoring, management, and control of communication systems. In recent years, machine learning approaches are becoming popular to improve the effectiveness of AMC for radio signals. However, the automatic modulation open-set recognition (AMOSR) scheme that aims to identify the known modulation types and recognize the unknown modulation signals is not well studied. Therefore, in this paper, we propose a novel multi-modal marginal prototype framework for radio frequency (RF) signals (MMPRF) to improve AMOSR performance. First, MMPRF addresses the problem of simultaneous recognition of closed and open sets by partitioning the feature space in the way of one vs. other and marginal restrictions. Secondly, we exploit the wireless signal domain knowledge to extract a series of signal-related features to enhance the AMOSR capability. In addition, we propose a GAN-based unknown sample generation strategy to allow the model to understand the unknown world. Finally, we conduct extensive experiments on several publicly available radio modulation data, and experimental results show that our proposed MMPRF outperforms the state-of-the-art AMOSR methods.

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Section: Comparison Of Auroc and Oscr Among Osr Baselinessupporting

confidence: 53%

The Importance of Expert Knowledge for Automatic Modulation Open Set Recognition

Li,

Wen,

Long

et al. 2023

IEEE Trans. Pattern Anal. Mach. Intell.

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show abstract

“…Finally, we obtained 11 ultra-long signal IQ sequences, and used the above formulas (3) and (4) to calculate the instantaneous amplitude A(t) and instantaneous phase w(t) of the signal, thereby converting the IQ sequence into an amplitude/phase (Aw) sequence. Since the essence of modulation is to load information into the amplitude or phase of the signal waveform, this transformation can IJWIS 20,3 bring out the inherent differences between different modulation types (Yunhao et al, 2022). By amplifying the differences between different modulation types, the separability of signals can be improved, thereby optimizing the classification task.…”

Section: Signal Preprocessing Methodsmentioning

confidence: 99%

TLN-LSTM: an automatic modulation recognition classifier based on a two-layer nested structure of LSTM network for extremely long signal sequences

Qian,

Tu,

Hou

et al. 2024

IJWIS

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Purpose Automatic modulation recognition (AMR) is a challenging problem in intelligent communication systems and has wide application prospects. At present, although many AMR methods based on deep learning have been proposed, the methods proposed by these works cannot be directly applied to the actual wireless communication scenario, because there are usually two kinds of dilemmas when recognizing the real modulated signal, namely, long sequence and noise. This paper aims to effectively process in-phase quadrature (IQ) sequences of very long signals interfered by noise. Design/methodology/approach This paper proposes a general model for a modulation classifier based on a two-layer nested structure of long short-term memory (LSTM) networks, called a two-layer nested structure (TLN)-LSTM, which exploits the time sensitivity of LSTM and the ability of the nested network structure to extract more features, and can achieve effective processing of ultra-long signal IQ sequences collected from real wireless communication scenarios that are interfered by noise. Findings Experimental results show that our proposed model has higher recognition accuracy for five types of modulation signals, including amplitude modulation, frequency modulation, gaussian minimum shift keying, quadrature phase shift keying and differential quadrature phase shift keying, collected from real wireless communication scenarios. The overall classification accuracy of the proposed model for these signals can reach 73.11%, compared with 40.84% for the baseline model. Moreover, this model can also achieve high classification performance for analog signals with the same modulation method in the public data set HKDD_AMC36. Originality/value At present, although many AMR methods based on deep learning have been proposed, these works are based on the model’s classification results of various modulated signals in the AMR public data set to evaluate the signal recognition performance of the proposed method rather than collecting real modulated signals for identification in actual wireless communication scenarios. The methods proposed in these works cannot be directly applied to actual wireless communication scenarios. Therefore, this paper proposes a new AMR method, dedicated to the effective processing of the collected ultra-long signal IQ sequences that are interfered by noise.

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“…They then jointly optimized these two types of losses to train the network. Shi et al [25] introduced a spatiotemporal autoencoder for AMC, capable of not only realizing unsupervised feature extraction but also enabling semi-supervised classification. Xie er al.…”

Section: B Related Deep Learning Workmentioning

confidence: 99%

STTMC: A Few-Shot Spatial Temporal Transductive Modulation Classifier

Shi,

Xu,

et al. 2024

Trans. Mach. Learn. Comm. Netw.

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The advancement of deep learning (DL) techniques has led to significant progress in Automatic Modulation Classification (AMC). However, most existing DL-based AMC methods require massive training samples, which are difficult to obtain in non-cooperative scenarios. The identification of modulation types under small sample conditions has become an increasingly urgent problem. In this paper, we present a novel few-shot AMC model named the Spatial Temporal Transductive Modulation Classifier (STTMC), which comprises two modules: a feature extraction module and a graph network module. The former is responsible for extracting diverse features through a spatiotemporal parallel network, while the latter facilitates transductive decision-making through a graph network that uses a closed-form solution. Notably, STTMC classifies a group of test signals simultaneously to increase stability of few-shot model with an episode training strategy. Experimental results on the RadioML.2018.01A and RadioML.2016.10A datasets demonstrate that the proposed method perform well in 3way-Kshot, 5way-Kshot and 10way-Kshot configurations. In particular, STTMC outperforms other existing AMC methods by a large margin.

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ConvLSTMAE: A Spatiotemporal Parallel Autoencoders for Automatic Modulation Classification

Cited by 16 publications

References 18 publications

The Importance of Expert Knowledge for Automatic Modulation Open Set Recognition

The Importance of Expert Knowledge for Automatic Modulation Open Set Recognition

TLN-LSTM: an automatic modulation recognition classifier based on a two-layer nested structure of LSTM network for extremely long signal sequences

STTMC: A Few-Shot Spatial Temporal Transductive Modulation Classifier

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