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.

Affine Frequency Division Multiplexing (AFDM) is an advanced communication waveform designed specifically for time-varying channels. This chirp-based multicarrier modulation technique is computationally efficient, which enables compact sonar implementations while achieving robust sensing performance through flexible parameter adjustments. Such characteristics make AFDM well-suited for Integrated Systems for Underwater Detection and Communication (ISUDC). In this paper, we explore an AFDM-based ISUDC system and propose a waveform model capable of transmitting multiple symbols to increase data capacity. We derive the Wideband Ambiguity Function (WAF) for this waveform and enhance it using complementary sequence coding, which reduces sensitivity to WAF variations and improves detection accuracy. Simulation results demonstrate that the proposed AFDM-based ISUDC waveform, featuring both multi-symbol support and complementary sequence coding, increases data capacity and improves communication bit error rate (BER) compared to traditional ISUDC waveforms. Additionally, the optimized WAF achieves enhanced detection performance, fulfilling critical ISUDC requirements.

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.

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.