Adaptive modulation and coding (AMC) is an effective technique for mitigating the severe dynamics of underwater acoustic (UWA) channels. However, its performance strongly relies on accurate channel state prediction, and prediction errors can cause significant performance degradation. Most existing approaches primarily focus on time-domain (TD) prediction, but the rapid variability of UWA channels makes long-term prediction highly challenging and limits their effectiveness. To overcome this limitation, we exploit the relative stability and sparsity of channel parameters in the delay–Doppler (DD) domain and propose a multi-scale convolutional long short-term memory (ConvLSTM) prediction framework. The DD-domain formulation reduces model complexity and supports multi-step forecasting, while the multi-scale architecture captures channel dynamics across different temporal scales and structural levels. The effectiveness of our method is validated through extensive experiments on real-world UWA measurements. The proposed framework achieves a normalized mean square error (NMSE) of 0.0818 in the DD-domain, significantly lower than the 0.2196 NMSE of direct TD prediction. Compared to conventional predictors, the proposed framework consistently reduces NMSE by approximately 70% across multiple datasets, thereby enhancing the robustness and feasibility of long-horizon channel forecasting for AMC systems.

Acquiring channel information in advance is crucial for improving underwater acoustic (UWA) communication networks, yet long propagation delays and severely limited bandwidth make accurate channel acquisition at both the transmitter (Tx) and receiver (Rx) difficult. Channel knowledge maps (CKMs) have recently shown strong benefits in terrestrial wireless systems by providing location-indexed channel information (e.g., gain, delay, AoA/AoD, or even channel state information (CSI) ), but have not been systematically studied for UWA channels, whose propagation characteristics differ fundamentally from radio channels. In this work, we develop an any-to-any (X2X) UWA CKM construction framework by leveraging the normal mode model for low-frequency UWACs. Specifically, we construct a UWA CKM that maps Tx–Rx positions to channel priors, and propose a CKM construction framework together with channel gain map (CGM), CKM-assisted channel estimation and beamforming schemes. Numerical simulations and SWellEx-96 S5 experimental data demonstrate that the reconstruction normalized mean square error (NMSE) remains below 0.05 when the signal-to-noise ratio (SNR) exceeds 12 dB, thereby validating the accuracy of the normal mode model and the efficacy of the mode extraction process. Furthermore, a high-fidelity CGM is constructed even in data-sparse scenarios, requiring only one-fourth of the data volume compared to conventional data-driven CGMs. Simulation results further indicate that the CKM-assisted beamforming achieves a superior array gain over baseline methods. At an SNR of 15 dB, the CKM-assisted channel estimation attains an NMSE of below -16 dB in shallow-water and below -21 dB in deep-water scenarios, significantly outperforming the baselines.

Orthogonal time frequency space (OTFS) modulation has garnered significant interest for its robust performance in fast time-varying channels, making it suitable for mobile underwater acoustic (UWA) communication system. This article introduces OTFS modulation to the UWA system and proposes a low-complexity minimum mean-squared error (MMSE) turbo equalization method. Leveraging the characteristics of UWA channels in the delay-Doppler (DD) domain, the method employs symbol-level MMSE equalization. By focusing processing on signals within the DD domain’s interference range, it reduces the channel matrix size, thereby lowering complexity. Given the long delay spread and large Doppler shift of UWA channels, symbol-level MMSE equalization inherently involves high complexity. To mitigate this, we propose two methods to further reduce the computational load associated with matrix inversion. First, we utilize common blocks in the channel matrix and employ a block iterative matrix inversion algorithm to retain computational results, thereby avoiding repeated inversions of the large dimensional matrix. Additionally, we enhance the diagonal dominance property of the channel matrix using the discrete Fourier transform (DFT) matrix. Subsequently, we approximate the inversion using the second-order Neumann series decomposition, further lowering computational complexity. Simulation results and experimental validations at Danjiangkou Lake demonstrate the efficacy of the proposed low-complexity iterative equalization algorithm.

Integrated systems for underwater detection and communication (ISUDC) plays a pivotal role in improving sonar integration and efficiency and has become a key research focus. This article tackles the underwater doubly dispersive wireless channel (DDWC) by introducing a novel transmitter side waveform design and a receiver side channel estimation algorithm based on affine frequency division multiplexing (AFDM) within the ISUDC framework. At the transmitter we employ AFDM as the core signal and target minimization of weighted sidelobes in the wideband ambiguity function (WAF). We use numerical analysis to quantify coding effects on the WAF and apply optimized random phase perturbations in P4 encoding via particle swarm optimization (PSO) to enhance detection and improve time Doppler resolution. At the receiver we develop a sparse channel estimation method based on an affine Fourier dictionary, which uses pilot signals to estimate phase perturbations and exploits delay-Doppler sparsity to improve accuracy in dynamic underwater environments while reducing multipath interference. We also derive new bounds on the pairwise error probability (PEP) for underwater acoustic DDWC, including numerical lower bounds and Chernoff upper bounds. Simulations demonstrate that jointly optimizing waveform design and channel estimation reduces PEP and normalized mean-square error (NMSE), provides superior detection for consecutive identical coded symbols and yields an ideal “thumbtack” shaped WAF. The proposed framework delivers a reliable and efficient solution for ISUDC in complex underwater environments.

We propose a Taylor expansion multiple signal classification (TE MUSIC) method for joint direction of departure (DOD) and direction of arrival (DOA) estimation in a bistatic multiple-input multiple-output (MIMO) array. First, using a Taylor expansion of the steering vector, a two-dimensional (2D) search in the conventional MUSIC method for MIMO arrays is reduced to a two-step one-dimensional (ID) search in the proposed TE MUSIC method. Second, DOAs of the targets can be achieved via Lagrange multiplier by a ID search. Finally, substituting the DOA estimates into the 2D MUSIC spectrum function, DODs of the targets are obtained by another ID search. Thus, the DOD and DOA estimates can be automatically paired. The performance of the proposed method is better than that of the MIMO ESPRIT method, and is similar to that of the 2D MUSIC method. Furthermore, due to the ID search, the TE MUSIC method avoids the high computational complexity of the 2D MUSIC method. Simulation results are presented to show the effectiveness of the proposed method.

Driven by the huge demand to explore oceans, underwater wireless communications have been rapidly developed in the past few decades. Due to the complex physical characteristics of water, acoustic wave is the only media available for underwater wireless communication at any distance. As a result, underwater acoustic communication (UAC) is the major research field in underwater wireless communication. In this paper, characteristics of underwater acoustic channels are first introduced and compared with terrestrial communication to demonstrate the difficulties in UAC research. To give a general impression of the UAC, current important research areas are mentioned. Furthermore, different principal modulation-based schemes for short- and medium-range communications with high data rates are investigated and summarized. To evaluate the performance of UAC systems in general, three criteria are presented based on the research publications and our years of experience in high-rate short- to medium-range communications. These three criteria provide useful tools to generally guide the design and evaluate the performance of underwater acoustic communication systems.

Complementary code keying (CCK) is a high rate spread spectrum coded modulation designed for frequency selective channels. CCK has been shown as an effective signaling technology in underwater communications. To improve the performance of CCK receivers, this work presents a joint channel estimation and detection receiver based on a Markov Chain Monte Carlo (MCMC) approach. We simplify the receiver complexity by introducing a reduced state detector based on the concept of set partitioning for the CCK modulation and incorporated with the MCMC channel estimation mechanism. The proposed method demonstrates significant performance gain at modest computational complexity over several existing receiver algorithms.

This study proposes a novel approach to joint channel estimation and detection of orthogonal frequency division multiplexing transmission over underwater acoustic (UWA) multipath channels exhibiting cluster sparsity. Unlike most sparse channel estimations, the authors exploit the cluster‐sparsity characteristic of UWA channels without additional prior information. They adopt a modified spike‐and‐slab prior model in their non‐parametric Bayesian learning framework. To avoid the need for a closed‐form Bayesian estimate, they apply the Markov chain Monte Carlo technique to joint achieve channel estimation and signal detection. The proposed solution is amenable to being integrated with soft‐input soft‐output decoding to improve the performance through turbo iteration. Simulation results demonstrate improved bit error rate of the proposed algorithm over existing algorithms.

Single-carrier with frequency domain equalization (SC-FDE) has been considered for bandwidth efficiency underwater acoustic (UWA) communication recently due to its reduced computational complexity and low peak-to-average power ratio. A multi-channel time-frequency domain equalization method for pseudurandom noise (PN) based SC-FDE is proposed in this paper. The proposed equalizer includes a multi-channel frequency domain equalizer followed by a low order multi-channel adaptive time domain decision feedback equalizer (DFE). The proposed algorithm is applied to the real data receiving from a lake test conducted in November 2011. It is demonstrated that the uncoded error-free data rates of around 1500 and 3000 bps are achieved using one transmitter and six-channel receiving hydrophone array at a distance of 1.8 km. Experiment results shows that the performance can be enhanced by 4.5-5.5 dB in terms of output signal-to-noise ratio (SNR).