Signal Modulation Recognition Algorithm Based on Improved Spatiotemporal Multi-Channel Network

Hou, Shunhu; Fan, Youchen; Han, Bing; Li, Yuhai; Fang, Shengliang

doi:10.3390/electronics12020422

Cited by 8 publications

(6 citation statements)

References 37 publications

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“…This limits the overall recognition accuracy of the network. In our previous study, there was also the problem of difficult distinction between WBFM and AM-DSB signals 14 .…”

Section: Introductionmentioning

confidence: 99%

“…References 5 – 13 propose single network identification algorithms, which are advanced, but all suffer from signal confusion problems. The literatures 14 – 17 propose improved single networks, which still have signal confusion problems, although the overall recognition accuracy has been improved. It is difficult for these single networks to accurately extract the unique features of all signals and achieve accurate classification.…”

Section: Introductionmentioning

confidence: 99%

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Multi-domain-fusion deep learning for automatic modulation recognition in spatial cognitive radio

Hou

Dong

et al. 2023

Sci Rep

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Automatic modulation recognition (AMR) is a critical technology in spatial cognitive radio (SCR), and building high-performance AMR model can achieve high classification accuracy of signals. AMR is a classification problem essentially, and deep learning has achieved excellent performance in various classification tasks. In recent years, joint recognition of multiple networks has become increasingly popular. In complex wireless environments, there are multiple signal types and diversity of characteristics between different signals. Also, the existence of multiple interference in wireless environment makes the signal characteristics more complex. It is difficult for a single network to accurately extract the unique features of all signals and achieve accurate classification. So, this article proposes a time–frequency domain joint recognition model that combines two deep learning networks (DLNs), to achieve higher accuracy AMR. A DLN named MCLDNN (multi-channel convolutional long short-term deep neural network) is trained on samples composed of in-phase and quadrature component (IQ) signals, to distinguish modulation modes that are relatively easy to identify. This paper proposes a BiGRU3 (three-layer bidirectional gated recurrent unit) network based on FFT as the second DLN. For signals with significant similarity in the time domain and significant differences in the frequency domain that are difficult to distinguish by the former DLN, such as AM-DSB and WBFM, FFT (Fast Fourier Transform) is used to obtain frequency domain amplitude and phase (FDAP) information. Experiments have shown that the BiGUR3 network has superior extraction performance for amplitude spectrum and phase spectrum features. Experiments are conducted on two publicly available datasets, the RML2016.10a and RML2016.10b, and the results show that the overall recognition accuracy of the proposed joint model reaches 94.94% and 96.69%, respectively. Compared to a single network, the recognition accuracy is significantly improved. At the same time, the recognition accuracy of AM-DSB and WBFM signals has been improved by 17% and 18.2%, respectively.

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“…This limits the overall recognition accuracy of the network. In our previous study, there was also the problem of difficult distinction between WBFM and AM-DSB signals 14 .…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-domain-fusion deep learning for automatic modulation recognition in spatial cognitive radio

Hou

Dong

et al. 2023

Sci Rep

Self Cite

View full text Add to dashboard Cite

show abstract

“…This limits the overall recognition accuracy of the network. In our previous study, there was also the problem of difficult distinction between WBFM and AM-DSB signals [22]. Therefore, in this paper, we propose a time-frequency domain joint recognition model that combines two deep learning networks, the MCLDNN and BiGRU3, to achieve higher accuracy AMR.…”

Section: Introductionmentioning

confidence: 99%

Multi-domain-fusion Deep Learning for Automatic Modulation Recognition in Spatial Cognitive Radio

Hou

Dong

et al. 2023

Preprint

Self Cite

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Automatic modulation recognition (AMR) is a critical technology in spatial cognitive radio(SCR), and building high-performance AMR model can achieve high classification accuracy of signals. AMR is a classification problem essentially, and deep learning has achieved excellent performance in various classification tasks. However, most deep learning models only use a single network. In complex wireless environments, due to the diversity of signal features, it is difficult for a single network to accurately extract the unique features of all signals and achieve accurate classification. So, this article proposes a time-frequency domain joint recognition model that combines two deep learning networks(DLNs), to achieve higher accuracy AMR. A DLN named MCLDNN(multi-channel convolutional long short-term deep neural network) is trained on samples composed of in-phase and quadrature component(IQ) signals, to distinguish modulation modes that are relatively easy to identify. This paper proposes a BiGRU3(three-layer bidirectional gated recurrent unit) network based on FFT as the second DLN. For signals with significant similarity in the time domain and significant differences in the frequency domain that are difficult to distinguish by the former DLN, such as AM-DSB and WBFM, FFT(Fast Fourier Transform) is used to obtain frequency domain amplitude and phase(FDAP) information. Experiments have shown that the BiGUR3 network has superior extraction performance for amplitude spectrum and phase spectrum features. Experiments are conducted on two publicly available datasets, the RML2016.10a and RML2016.10b, and the results show that the overall recognition accuracy of the proposed joint model reaches 94.94% and 96.69%, respectively. Compared to a single network, the recognition accuracy is significantly improved. At the same time, the recognition accuracy of AM-DSB and WBFM signals has been improved by 17% and 18.2%, respectively.

show abstract

“…Likelihood-based approaches, although typically characterized by higher accuracy, often require substantial amounts of prior information and higher computational complexity. Feature-based methods rely on extracting key features from the signals, such as higher-order accumulations [2], sequence features [3], and image features [4]. These methods generally exhibit higher computational efficiency, and their recognition results tend to be more interpretable and analyzable.…”

Section: Introductionmentioning

confidence: 99%

VLCMnet-Based Modulation Format Recognition for Indoor Visible Light Communication Systems

Zheng,

He,

Zhang

et al. 2024

Photonics

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In indoor visible light communication (VLC), the received signals are subject to severe interference due to factors such as high-brightness backgrounds, long-distance transmissions, and indoor obstructions. This results in an increase in misclassification for modulation format recognition. We propose a novel model called VLCMnet. Within this model, a temporal convolutional network and a long short-term memory (TCN-LSTM) module are utilized for direct channel equalization, effectively enhancing the quality of the constellation diagrams for modulated signals. A multi-mixed attention network (MMAnet) module integrates single- and mixed-attention mechanisms within a convolutional neural network (CNN) framework specifically for constellation image classification. This allows the model to capture fine-grained spatial structure features and channel features within constellation diagrams, particularly those associated with high-order modulation signals. Experimental results obtained demonstrate that, compared to a CNN model without attention mechanisms, the proposed model increases the recognition accuracy by 19.2%. Under severe channel distortion conditions, our proposed model exhibits robustness and maintains a high level of accuracy.

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Signal Modulation Recognition Algorithm Based on Improved Spatiotemporal Multi-Channel Network

Cited by 8 publications

References 37 publications

Multi-domain-fusion deep learning for automatic modulation recognition in spatial cognitive radio

Multi-domain-fusion deep learning for automatic modulation recognition in spatial cognitive radio

Multi-domain-fusion Deep Learning for Automatic Modulation Recognition in Spatial Cognitive Radio

VLCMnet-Based Modulation Format Recognition for Indoor Visible Light Communication Systems

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