The impulsive noise and the time-varying channel are two major detrimental factors which greatly constrain the performance of underwater acoustic communications (UACs). Utilizing the joint sparsity of the impulsive noise and the channel impulse response, the paper proposes the generalized approximate message passing (GAMP)-based sparse Bayesian learning (SBL) frameworks for joint impulsive noise mitigation and channel estimation and tracking for orthogonal frequency division multiplexing (OFDM) UACs. Firstly, the SBL framework for the joint estimation is employed. To reduce the computational complexity, the GAMP is introduced into the expectation-maximization (EM) algorithm, and a low-complexity GAMP based SBL framework is formulated without performance degradation. To further estimate and track the impulsive noise and channel state information in the slow time-varying scenarios, we propose a novel GAMP-based temporal SBL framework. The factor graph and GAMP are used to achieve the approximated estimation for the posterior statistics of both the channel state information and the impulsive noise. The algorithm formulates the message passing scheduling for the EM algorithm to solve the joint multiple sparse signal recovery problem. Simulations and sea-trial results demonstrate that the proposed algorithms significantly improve the performance in terms of the mean square error of channel estimation, impulsive noise estimation, bit error rate and computational complexity compared with their corresponding SBL-based counterparts.

Noise fitting plays a key role in underwater acoustic communications. Traditional approximate models can fit global heavy-tail distribution of the impulsive noise with fixed parameters. These models are unable to cover local distributions with arbitrary lengths. In this paper, we propose a generative-adversarial-network-based underwater noise simulator (GUNS), which constructs a deep-neural-network-based generator and a convolutional-neural-network-based discriminator are constructed to learn the heavy-tail distribution of the impulsive noise. Based on the noise collected in the Wuyuanwan Bay, Xiamen, probability distribution function of the underwater acoustic noise simulated by the proposed GUNS performs lower Kullback-Leibler divergence, Jensen-Shannon divergence and mean square error than that employed traditional approximate models.

Underwater acoustic (UWA) channels present diverse sparsity, which is challenging for UWA communications and underwater networked applications. Conventional methods fail to adapt to the uncertain sparsity due to inflexible adjustment rules or improper assumptions. This paper investigates the estimation of channels with uncertain sparsity and model-driven deep learning (DL) based estimation networks are proposed. We construct the channel estimation as sparse signal recovery model and exploit the channel sparsity based on the approximate message passing (AMP) method. Then the learned AMP network with soft-thresholding shrinkage function (ST-LAMP) is designed as a generalized estimator and suitable to random sparse UWA channels. Moreover, to achieve the Bayesian optimal solution, Gaussian mixture (GM) prior is imposed for UWA channels and the GM-LAMP network is formulated with the derived shrinkage function based on the minimum mean squared error criterion. The proposed networks incorporate the learning ability of DL methods by exploiting marine data and the domain knowledge from the classical model to realize parameter configuration and adaptive optimization. Experimental results verify that the proposed networks outperform benchmarks over UWA channels with diverse sparsity in terms of adaptability and effectiveness. Especially, real tests present the feasibility of proposed networks for on-line deployments.

In this letter, a new method on the ${K}$ -factor estimation is proposed for wireless communications over Rician frequency-flat fading channels. Firstly, the mean and variance of the squared envelope of the received signal are respectively derived. Then, we employ them to further derive the closed-form expression for the ${K}$ -factor and finally present its estimation formula. Performance analyses, supported by Monte Carlo simulation results for a range of channel conditions, reveal that our proposed estimator outperforms prior techniques, and illustrate the approximate robustness of our suggested work to the signal-to-noise ratio, maximum Doppler shift, and angle of arrival without considering a priori knowledge.

Ocean sound speed is important for underwater acoustic applications, such as communications, navigation and localization, where the assumption of uniformly distributed sound speed profiles (SSPs) is generally used and greatly degrades the performance of underwater acoustic systems. The acquisition of SSPs is necessary for the corrections of the sound ray propagation paths. However, the inversion of SSPs is challenging due to the intricate relations of interrelated physical ocean elements and suffers from the high costs of calculations and hardware deployments. This paper proposes a novel sound speed inversion method based on multi-source ocean remote sensing observations and machine learning, which adapts to large-scale sea regions. Firstly, the datasets of SSPs are generated utilizing the Argo thermohaline profiles and the empirical formulas of the sound speed. Then, the SSPs are analyzed utilizing the empirical orthogonal functions (EOFs) to reduce the dimensions of the feature space as well as the computational load. Considering the nonlinear regression relations of SSPs and the observed datasets, a general framework for sound speed inversion is formulated, which combines the designed machine learning models with the reduced-dimensional feature representations, multi-source ocean remote sensing observations and water temperature data. After being well trained, the proposed machine learning models realize the accurate inversion of the targeted ocean region by inputting the real-time ocean environmental data. The experiments verify the advantages of the proposed method in terms of the accuracy and effectiveness compared with conventional methods.