Optimal Bidirectional LSTM for Modulation Signal Classification in Communication Systems

Hamza, Manar Ahmed; Hassine, Siwar Ben Haj; Larabi-Marie-Sainte, Souad; Nour, Mohamed K.; Al‐Wesabi, Fahd N.; Motwakel, Abdelwahed; Hilal, Anwer Mustafa; Duhayyim, Mesfer Al

doi:10.32604/cmc.2022.024490

Cited by 5 publications

(2 citation statements)

References 26 publications

(22 reference statements)

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“…To demonstrate the effectiveness of the proposed AMC method based on GCN, we compared the performance of the proposed method with those state-of-the-art AMC methods. The achieved methods include deep learning methods (basic CNN [44], InceptionV3 [45], GAN [29], VGGnet [30], ResNet [46,47], LSTM [48,49], deep complex network (DCN) [1]), and feature extraction methods (HOC [3,4] using an SVM classifier, CS [50] with a neural network classifier, and continuous wavelet transform (CWT) [11,51] with an SVM classifier). We carried out the comparison experiments in Ch1 and Ch2, respectively.…”

Section: The Analysis Of the Influence Of The Different Featuresmentioning

confidence: 99%

Feature Fusion Based on Graph Convolution Network for Modulation Classification in Underwater Communication

Yao

Yang

Sheng

2023

Entropy

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Automatic modulation classification (AMC) of underwater acoustic communication signals is of great significance in national defense and marine military. Accurate modulation classification methods can make great contributions to accurately grasping the parameters and characteristics of enemy communication systems. While a poor underwater acoustic channel makes it difficult to classify the modulation types correctly. Feature extraction and deep learning methods have proven to be effective methods for the modulation classification of underwater acoustic communication signals, but their performance is still limited by the complex underwater communication environment. Graph convolution networks (GCN) can learn the graph structured information of the data, making it an effective method for processing structured data. To improve the stability and robustness of AMC in underwater channels, we combined the feature extraction and deep learning methods by fusing the multi-domain features and deep features using GCN. The proposed method takes the relationships among the different multi-domain features and deep features into account. Firstly, a feature graph was built using the properties of the features. Secondly, multi-domain features were extracted from the received signals and deep features were extracted from the signals using a deep neural network. Thirdly, we constructed the input of GCN using these features and the graph. Then, the multi-domain features and deep features were fused by the GCN. Finally, we classified the modulation types using the output of GCN by way of a softmax layer. We conducted the experiments on a simulated dataset and a real-world dataset, respectively. The results show that the AMC based on GCN can achieve a significant improvement in performance compared to the current state-of-the-art methods. Our approach is robust in underwater acoustic channels.

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Section: The Analysis Of the Influence Of The Different Featuresmentioning

confidence: 99%

Feature Fusion Based on Graph Convolution Network for Modulation Classification in Underwater Communication

Yao

Yang

Sheng

2023

Entropy

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“…In the 2000s, deep learning techniques emerged as a powerful tool for face recognition, leading to significant improvements in accuracy and efficiency [5]. Deep learning algorithms, such as convolutional neural networks (CNNs) [6], can learn complex representations of facial features from large data sets, enabling them to recognize faces under a wide range of conditions [7]. Today, face recognition technology is used in a wide range of applications, from security and surveillance [8] to entertainment [9] and social media.…”

Section: Introductionmentioning

confidence: 99%

Automatic Player Face Detection and Recognition for Players in Cricket Games

Haq,

Sethi,

Ahmad

et al. 2024

IEEE Access

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In this Paper, we create an augmented reality cricket broadcasting application that uses player recognition and automatic detection during play to display player personal data. The system uses AdaBoost to detect player and player face and use PAL based face recognition model to recognize the faces of players on the field. The system is trained on a large dataset of cricket game footage and achieves high accuracy in detecting and recognizing players' faces even with several conditions such as occlusion, non-uniform illumination and pose variation. The system has the potential to enhance the viewing experience of cricket games by providing real-time player identification and statistics. The system can also be used in other sports to provide similar benefits. The paper discusses the system's methodology, results, and implications for the future of sports broadcasting. Overall, the system provides a promising solution for automatic player face detection and recognition in sports broadcasting.

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Automatic Modulation Classification for Underwater Acoustic Communication Signals Based on Deep Complex Networks

Yao

Yang

Sheng

2023

Entropy

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Automatic modulation classification (AMC) is an important method for monitoring and identifying any underwater communication interference. Since the underwater acoustic communication scenario is full of multi-path fading and ocean ambient noise (OAN), coupled with the application of modern communication technology, which is usually susceptible to environmental influences, automatic modulation classification (AMC) becomes particularly difficult when it comes to an underwater scenario. Motivated by the deep complex networks (DCN), which have an innate ability to process complex data, we explore DCN for AMC of underwater acoustic communication signals. To integrate the signal processing method with deep learning and overcome the influences of underwater acoustic channels, we propose two complex physical signal processing layers based on DCN. The proposed layers include a deep complex matched filter (DCMF) and deep complex channel equalizer (DCCE), which are designed to remove noise and reduce the influence of multi-path fading for the received signals, respectively. Hierarchical DCN is constructed using the proposed method to achieve better performance of AMC. The influence of the real-world underwater acoustic communication scenario is taken into account; two underwater acoustic multi-path fading channels are conducted using the real-world ocean observation dataset, white Gaussian noise, and real-world OAN are used as the additive noise, respectively. Contrastive experiments show that the AMC based on DCN can achieve better performance than the traditional deep neural network based on real value (the average accuracy of the DCN is 5.3% higher than real-valued DNN). The proposed method based on DCN can effectively reduce the influence of underwater acoustic channels and improve the AMC performance in different underwater acoustic channels. The performance of the proposed method was verified on the real-world dataset. In the underwater acoustic channels, the proposed method outperforms a series of advanced AMC method.

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Optimal Bidirectional LSTM for Modulation Signal Classification in Communication Systems

Cited by 5 publications

References 26 publications

Feature Fusion Based on Graph Convolution Network for Modulation Classification in Underwater Communication

Feature Fusion Based on Graph Convolution Network for Modulation Classification in Underwater Communication

Automatic Player Face Detection and Recognition for Players in Cricket Games

Automatic Modulation Classification for Underwater Acoustic Communication Signals Based on Deep Complex Networks

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