The process of gathering of scientific data plays an important role in telemonitoring and communications technologies of underwater information. However, to obtain such a huge data in underwater-wireless-sensor-networks (UWSNs), conventional methods often fail in energy efficiency. Compressive sampling (CS) provides a new perspective to solve the problem. Unfortunately, the underwater acoustic signal is non-sparse in the time domain and the current CS methods cannot be used directly. This study adopts the discrete cosine transform (DCT) based dictionary-matrix for sparse representation. In addition, the measurement matrix is optimized via the steepest descent method for more efficient sampling. Then we introduce an approach based on an approximated l0 (AL0) norm at the receiving terminal, to search the sparse solution via the steepest descent method and projections. Combing with the measurement matrix and dictionary-matrix, the sparse estimation is used for reconstruction. Experimental results confirm the superior performances of the strategies of the proposed compressive sampling and reconstruction than those of the traditional measurement matrix and reconstruction methods including matching pursuit (MP) and orthogonal matching pursuit (OMP) methods.

Channel estimation in Underwater Acoustic Communication (UAC) faces significant challenges due to the non-Gaussian, impulsive noise in ocean environments and the inherent high dimensionality of the estimation task. This paper introduces a robust channel estimation algorithm by solving an ${\mathcal{l}}_1-{\mathcal{l}}_1$ optimization problem via the Alternating Direction Method of Multipliers (ADMM), effectively exploiting channel sparsity and addressing impulsive noise outliers. A non-monotone backtracking line search strategy is also developed to improve the convergence behavior. The proposed algorithm is low in complexity and has robust performance. Simulation results show that it exhibits a small performance deterioration of less than 1 dB for Channel Impulse Response (CIR) estimation in impulsive noise environments, nearly matching its performance under Additive White Gaussian Noise (AWGN) conditions. For Delay-Doppler (DD) doubly spread channel estimation, it maintains Bit Error Rate (BER) performance comparable to using ground truth channel information in both AWGN and impulsive noise environments. At-sea experimental validations for channel estimation in Orthogonal Frequency Division Multiplexing (OFDM) systems further underscore the fast convergence speed and high estimation accuracy of the proposed method.

Multiple Frequency-Shift Keying (MFSK) has been used widely for underwater acoustic communications due to its low complexity and channel robustness. However, the traditional MFSK has the limitation of a low bit rate compared with coherent acoustic communication. To increase the bit rate, this study designs a new MFSK with the concept of orthogonal frequency division multiplexing (OFDM). We also adopt a channel-concatenated coding to resist the multipath interference and design the iterative joint decoding. The channel-concatenated coding consists of a Hadamard code and a convolutional code. Correspondingly, the iterative joint decoding uses the Hadamard–Viterbi joint soft decoding framework with a newly designed branch metric, which uses the Hadamard structure. As an important preprocessing link for a received signal, frame synchronization and Doppler compensation are also described in detail in this study. Simulations and experiments are conducted to show the effectiveness of the proposed MFSK underwater acoustic communications.

In the rapidly advancing field of Underwater Acoustic Communication (UAC), channel prediction remains a major challenge, exacerbated by the complicated nature of ocean environments. This paper introduces an innovative Multi-Task Learning (MTL) framework for time-varying Underwater Acoustic (UWA) channel prediction. By decomposing the high-dimensional Channel Impulse Response (CIR) prediction into interconnected tasks, the proposed framework leverages a Shared Feature Learning (SFL) layer, capturing intricate dependencies underlying UWA channels. To validate its efficacy, we conducted thorough evaluations, leveraging real-world data from two distinct at-sea experiments conducted in Wuyuan Bay, China. A comprehensive comparative study of various configurations for the SFL layer, ranging from commonly used Recurrent Neural Network (RNN)-based models to the more advanced transformer structure, further underscores the flexibility and broad applicability of our MTL framework for handling various challenging UWA environments.

The higher-order statistics based independent component analysis (ICA) algorithm can extract natural image features. Based on the maximum likelihood ICA criterion, and using the weighted orthogonal constrained natural gradient, a new ICA algorithm is proposed. Natural image feature extraction simulation results show that, compared with other ICA algorithms, the proposed algorithm has faster convergence rate, most of the extracted basis vectors are localized in space, frequency, and orientation, which can describe the features of the natural images well, and the corresponding coefficients are very sparse, obey stronger super-Gaussian distribution with very high kurtosis.