To mitigate the error propagation of single-carrier time-domain equalization (SC-TDE) with insufficient observation data, this article proposes a low-complexity message-passing-based SC-TDE algorithm. First, the temporal correlation (TC) between subblocks is exploited to improve the performance of conventional message-passing-based sparse Bayesian learning (SBL) when the data are insufficient. The proposed algorithm then models the channel estimation process as a hidden Markov model. It captures the TC property by utilizing a first-order autoregressive model, thus supporting the current subblock with a priori information from the previous subblock. By using belief propagation (BP), the TC and BP-based SBL algorithm (TC-BP-SBL) is derived, which is then approximated to obtain the TC and approximation-message-passing-based SBL (TC-AMP-SBL) with lower computational complexity. Finally, taking advantage of AMP and expectation propagation (EP), a two-layer iterative equalization algorithm is introduced for joint message passing. The inner iteration uses AMP for symbol estimation, and the outer iteration improves the equalization performance by EP based on deterministic approximate variational inference. The proposed algorithm is validated using data collected during the 11th Chinese Arctic Scientific Expedition. The results show that the proposed algorithm can significantly reduce the computational complexity of SC-TDE and effectively mitigate error propagation when the observation data are insufficient.

This paper describes a three-step frequency-domain equalization scheme for multiple-input multiple-output (MIMO) underwater acoustic communication. First, an iterative least-squares channel estimation method is developed to enhance the accuracy of channel estimation in MIMO communication. The interference rejection combining method is then adopted to suppress co-channel interference based on the estimated MIMO channels. This technique exploits the correlation between the interference received through different channels. Finally, a decision feedback equalizer embedded with a digital phase-lock loop is cascaded before the final determination of equalized symbols to compensate the phase rotation. Experimental results show that the bit error rates of the proposed scheme can be several orders of magnitude lower than those of conventional frequency-domain equalization schemes.

Underwater terrain image (UTI) matching provides a new idea for terrain-aided localization technology. However, the diversity of UTIs makes it very difficult to accurately match real-time terrain images with reference terrain images. To improve the localization accuracy of UTIs, this letter describes how UTI matching can be modeled as a deep learning problem based on supervised algorithms. Specifically, we propose a hierarchical matching strategy for the diverse features of UTIs by combining the deep and shallow feature characteristics of a convolutional neural network to improve the localization accuracy. We also propose a sample-generation strategy based on candidate area quality index classification, and develop a data enhancement method by simulating the underwater terrain environment to overcome the lack of network training data. Simulations prove that the proposed method achieves accurate and stable localization results.

The long propagation delay of acoustic links leads to the complex randomness of packet collision, which reduces the network packet delivery rate (PDR) and aggravates network congestion. A single vector hydrophone with directional reception characteristics can concentrate the reception gain on a certain direction, which can increase spatial reuse, reduce packet collision, and help to improve the performance of the underwater acoustic wireless sensor networks (UASNs). Herein, this paper proposes a cross-layer protocol with low interference and low congestion (CLIC) based on directional reception. An integrated routing-medium access control (MAC) design is also devised in the CLIC scheme to use the directional beams to create the least-interfering, highest-capacity data transmission links, weighing key factors affecting network performance to obtain routes with low collisions and low congestion. Simulation results show that the CLIC has a higher packet delivery rate (PDR) and higher energy efficiency compared to the QELAR, CITP, and VBF protocols.

Deep-sea horizontal acoustic channels are characterized by widely separated clusters, with inter-cluster intervals spanning several hundred milliseconds. This channel type leads to severe inter-block interference (IBI) in zero-padded single-carrier block transmissions. In this paper, we propose a two-step equalizer based on joint channel estimation (CE), IBI cancellation (IBIC), and symbol detection (SD), termed JCE-IBIC-SD. The proposed equalizer effectively suppresses IBI through iterative CE and symbol detection, even with insufficient training sequences. In the first step, considering the large separation between multipath clusters, the equalizer decomposes deep-sea channels longer than one block into quasi-synchronous channels and retrieves the symbols for all blocks through IBIC and equalization. In the second step, based on the a priori knowledge of the symbols, the JCE-IBIC-SD is applied. Using the damped Gaussian generalized approximate message passing sparse Bayesian learning algorithm, iteratively computes the maximum a posteriori estimates of both the channels and symbols through within a joint factor graph model. Simulations and deep-sea experimental results demonstrate that, even when the receiving array is located in the acoustic shadow zone, the proposed equalizer outperforms traditional IBIC equalizers with reasonable computational complexity.