IRLNet: A Short-Time and Robust Architecture for Automatic Modulation Recognition

Yang, Hua; Zhao, Lichen; Yue, Guangxue; Ma, Bolin; Li, Wei

doi:10.1109/access.2021.3121762

Cited by 8 publications

(8 citation statements)

References 35 publications

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“…Whereas, F. Liu [32] used GRU model achieving accuracy of 86% at 0 dB SNR and less than 55% at -16 dB SNR. H. Yang [47] used IRLNet model achieving accuracy of 97% at 5 dB SNR and less than 50% at -16 dB SNR. X. Xie [35] used DenseNet and BLSTM model achieving accuracy of 84% at 0 dB SNR and less than 25% at -16 dB SNR.…”

Section: Classification Performance Simulation Resultsmentioning

confidence: 99%

“…DeepSig radio signal datasets RadioML2016.10a [45] [46] and RadioML2016.10b [47] are used for evaluating the modulation recognition of our proposed models.…”

Section: Rml201610a and Rml201610b Radio Signal Datasetmentioning

confidence: 99%

“…A batch size of 128 is used on each training epoch [46]. The RML2016.10b dataset parameters [47]are shown in Table 6. It contains 1.2M samples, each represented using two vectors each of them has 128 elements Shape: (1200000, 2, 128) [48].…”

Section: Rml201610a and Rml201610b Radio Signal Datasetmentioning

confidence: 99%

“…K. Liu et al [20] combined DCN and BiLSTM models achieving an accuracy of 90% at high SNR. H. Yang et al [21] used an IRS model combined with LSTM neural networks achieving an accuracy of 90% at high SNR. V. Kumaran et al [22] combined GRU and BiLSTM neural networks achieving an accuracy of 92% at high SNR.…”

Section: Introductionmentioning

confidence: 99%

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Radio Modulation Classification Optimization Using Combinatorial Deep Learning Technique

Elkhatib,

Kamalov,

Moussa

et al. 2024

IEEE Access

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We present an automatic signal modulation classification model using combinatorial deep learning technique. Our proposed deep learning model increase accuracy for low Signal-to-Noise Ratio (SNR) and maintain a high classification accuracy for high SNR signals. Using a hybrid deep learning model combining both ConvLSTM with Transformer-block neural networks, the proposed modulation classifier architecture can learn the signal for both low and high SNR and get better accuracy for signals with high noise. The proposed deep learning modulation classification technique achieves improved classification accuracy of 66% for low SNR signals and 93.5% at high SNR showing that our model is robust under noisy signal modulation. Thus, getting better accuracy in lower SNR signals without sacrifice accuracy for higher SNR signals. An adaptive weighted focal loss function is proposed as an optimized loss function for efficient classification which can be used to control the outliers within a class imbalance and avoid underflow issues. Our deep learning radio modulation classification model works using raw signal without the need of denoising the noisy signal. INDEX TERMS Automatic modulation classification, dynamic spectrum allocation, deep learning techniques, transformer-block convLSTM, feature-based extraction, AI-based wireless communications.

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Section: Classification Performance Simulation Resultsmentioning

confidence: 99%

“…DeepSig radio signal datasets RadioML2016.10a [45] [46] and RadioML2016.10b [47] are used for evaluating the modulation recognition of our proposed models.…”

Section: Rml201610a and Rml201610b Radio Signal Datasetmentioning

confidence: 99%

Section: Rml201610a and Rml201610b Radio Signal Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Radio Modulation Classification Optimization Using Combinatorial Deep Learning Technique

Elkhatib,

Kamalov,

Moussa

et al. 2024

IEEE Access

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“…However, modulation modes recognition becomes more challenging when the signal is more complex [29]. Noise is a significant factor that affects the recognition of modulation modes as it alters the signal's characteristics, thereby impacting the recognition accuracy of modulation modes.…”

Section: Automatic Modulation Classificationmentioning

confidence: 99%

Semi-supervised-based automatic modulation classification with domain adaptation for wireless IoT spectrum monitoring

et al. 2023

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Dramatic advances in wireless networks have led to the Smart IoT, which may enable new modes of information transfer. Generally, data-driven advanced artificial intelligence (AI) techniques can be used in smart IoT networks, which prones to a lot of unexpected challenges. Enabling efficient management of spectrum resources based on cognitive radio is considered to be an effective means to address the limited spectrum resources. As a key technology in CR, automatic modulation recognition (AMR) is developing towards intelligence with deep learning as the main approach. Deep learning (DL) has been widely applied to AMR for possible improvement of recognition accuracy, while the superb performance highly depends on high-quality and well-labeled datasets. Consequently, these requirements prone to poor performance in the environments where the datasets are not well-labeled. Motivated by this, domain adaptation is considered for AMR in this paper, and a novel network architecture is proposed therein, termed semi-supervised automatic modulation recognition (SemiAMR). Specifically, source domain high SNR data are mapped by the source encoder to the classification domain and classified by the classifier, where the source data with labels enable this training process. Next, the discriminator and the target domain encoder are trained by determining whether the data is from the target or source domain. Finally, the target domain encoder and classifier are combined and are used to infer the data labels of the target domain. Experimental results state that the accuracy of proposed SemiAMR achieves 1\% to 27\% improvements when compared with classical schemes under the target domain condition where there is no corresponding labeled data and signal-to-noise ratios (SNRs) varies from −20 dB to −4 dB.

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Effects of diagram plane on neural network based modulation recognition

Leblebici,

Çalhan,

Cicioğlu

2024

Applied Soft Computing

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IRLNet: A Short-Time and Robust Architecture for Automatic Modulation Recognition

Cited by 8 publications

References 35 publications

Radio Modulation Classification Optimization Using Combinatorial Deep Learning Technique

Radio Modulation Classification Optimization Using Combinatorial Deep Learning Technique

Semi-supervised-based automatic modulation classification with domain adaptation for wireless IoT spectrum monitoring

Effects of diagram plane on neural network based modulation recognition

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