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In recent years, research on modulated signal recognition using deep learning (DL) has achieved remarkable success, allowing automatic modulation recognition (AMR) to play a crucial role in modern communication systems. The emergence of the attention mechanism has then rapidly led to a wide range of applications in image classification and speech recognition, which proves the effectiveness of the attention mechanism. In this paper, the authors propose a network model feed‐forward Attention mechanism with Residuals networks and Long Short‐Term Memory (RLADNN) based on feed‐forward attention mechanism with Residuals networks (Resnet) and Long Short‐Term Memory (LSTM), which takes the advantage that the Attention mechanism can effectively solve some long‐term memory problems in [−20:18] signal‐to‐noise ratio (SNR) for the recognition of 11 modulated signals with different SNR, and effectively improves the recognition rate. [ABSTRACT FROM AUTHOR]

To address the problems of the high complexity and poor bit error rate (BER) performance of traditional communication systems in underwater acoustic environments, this paper incorporates the theory of deep learning into a conventional communication system and proposes data-driven underwater acoustic filter bank multicarrier (FBMC) communications based on convolutional autoencoder networks. The proposed system is globally optimized by two one-dimensional convolutional (Conv1D) modules at the transmitter and receiver, it realizes signal reconstruction through end-to-end training, it effectively avoids the inherent imaginary interference of the system, and it improves the reliability of the communication system. Furthermore, dense-block modules are constructed between Conv1D layers and are connected across layers to achieve feature reuse in the network. Simulation results show that the BER performance of the proposed method outperforms that of the conventional FBMC system with channel equalization algorithms such as least squares (LS) estimation and virtual time reversal mirrors (VTRM) under the measured channel conditions at a certain moment in the Qingjiang River. [ABSTRACT FROM AUTHOR]

This letter presents the power of deep learning for signal detection in orthogonal frequency division multiplexing with index modulation (OFDM-IM) systems. In this letter, we propose the Y-shaped net-based scheme with fully connected layers (Y-FC) and with bidirectional long short-term memory units (Y-BLSTM) to further optimize the data reception. In the Y-shaped scheme, the active indices and the constellation demapping are learned by two parallel sub-neural networks respectively, the output features are then concatenated and flattened to obtain binary bits. The performance is evaluated under various system parameters, such as modulation methods, number of hidden neural nodes and channel states. Simulation results show that compared with the data-driven signal detection with direct input-output, the proposed scheme achieves several dB of bit-error-rate performance improvement with almost the same running time. [ABSTRACT FROM AUTHOR]

By contraposing the signal detection for filter bank multicarrier (FBMC) communications with the underwater acoustic (UWA) channel, this paper analyzes the traditional imaginary interference problem and proposes a deep learning-based method. The neural network with feature extraction and automatic learning ability is employed to replace the demodulation modules to recover transmitted signals without explicit channel estimation and equalization. Sufficient data sets are generated according to the measured channel conditions in Qingjiang river, the optimization of network parameters is finished by constraining cost function in offline training, and the signal detection is carried out directly with the well-trained network in online testing. The system performance of various supervised learning models such as multilayer perceptron (MLP), convolutional neural network (CNN), and bidirectional long short-term memory (BLSTM) network is compared under different data sizes, network parameters, and prototype filters. The simulation results show that the bit error rate (BER) performance of the proposed signal detection is better than that of the classic one, which indicates that deep learning is a promising tool in UWA communication systems. [ABSTRACT FROM AUTHOR]

Ship radiated noise is an important information source of underwater acoustic targets, and it is of great significance to the identification and classification of ship targets. However, there are a lot of interference noises in the water, which leads to the reduction of the model recognition rate. Therefore, the recognition results of radiated noise targets are severely affected. This paper proposes a machine learning Dempster–Shafer (ML-DS) decision fusion method. The algorithm combines the recognition results of machine learning and deep learning. It uses evidence-based decision-making theory to realize feature fusion under different neural network classifiers and improve the accuracy of judgment. First, deep learning algorithms are used to classify two-dimensional spectrogram features and one-dimensional amplitude features extracted from CNN and LSTM networks. The machine learning algorithm SVM is used to classify the chromaticity characteristics of radiated noise. Then, according to the classification results of different classifiers, a basic probability assignment model (BPA) was designed to fuse the recognition results of the classifiers. Finally, according to the classification characteristics of machine learning and deep learning, combined with the decision-making of D-S evidence theory of different times, the decision-making fusion of radiated noise is realized. The results of the experiment show that the two fusions of deep learning combined with one fusion of machine learning can significantly improve the recognition results of low signal-to-noise ratio (SNR) datasets. The lowest fusion recognition result can reach 76.01%, and the average fusion recognition rate can reach 94.92%. Compared with the traditional single feature recognition algorithm, the recognition accuracy is greatly improved. Compared with the traditional one-step fusion algorithm, it can effectively integrate the recognition results of heterogeneous data and heterogeneous networks. The identification method based on ML-DS proposed in this paper can be applied in the field of ship radiated noise identification. [ABSTRACT FROM AUTHOR]

This paper presents a convolutional neural network (CNN) based filter bank multicarrier (FBMC) system for underwater acoustic (UWA) communications. Different from traditional FBMC receivers that only detect the transmitted symbols after expliciting channel estimation and equalization, the proposed system takes a pre-trained CNN model as a receiver to recover the transmitted symbols directly and avoid the inherent imaginary interference. At the offline training stage, the CNN model takes the known transmitted data as the labels and the received data as input for iterative learning. At the testing stage, unlabeled received data are directly output to CNN to estimate the online transmitted symbols. The CNN based UWA FBMC system is analysed under various system parameters such as input permutations, convolution kernels and prototype filters, and sufficient communication data are generated according to the measured UWA channel impulse responses. Simulation results show that the proposed system achieves admirable performance for signal detection compared to the previous UWA FBMC system. [ABSTRACT FROM AUTHOR]