Underwater acoustic communication (UAC) is one of the core technologies for the Internet of Underwater Things (IoUT). Channel estimation (CE) is crucial for achieving reliable UAC performance. However, existing underwater acoustic CE methods involve a trade-off between computational overhead and estimation accuracy. To relieve this issue, this paper proposes an efficient and high-precision CE algorithm. Based on compressed sensing theory, the algorithm first obtains an on-grid delay estimate via orthogonal matching pursuit (OMP). Subsequently, we optimize the delay estimation result beyond the grid constraint using an anadromic gradient descent (AGD) algorithm combined with the Armijo backtracking line search approach. The simulation results demonstrate that the proposed AGD-Armijo algorithm achieves comparable CE performance to existing same type of off-grid CE algorithms, while improving the iterative convergence rate by approximately 52.7%. Furthermore, to address the pseudo-path estimation problem caused by off-grid delay errors during the on-grid estimation stage, we propose a two-stage optimization off-grid channel estimation (TSO-OGCE) method. The TSO-OGCE can effectively suppress pseudo-paths and achieve reliable estimation performance by introducing a single-path off-grid optimization step. We have demonstrated the feasibility and effectiveness of the above methods through field experiments at Songhua Lake.

This article investigates a direction-of-arrival (DOA) estimation method for underwater acoustic arrays in non-Gaussian impulsive noise environments. Traditional DOA estimation methods for underwater acoustic arrays typically presume that underwater environmental noise follows a Gaussian distribution. This assumption can lead to a significant degradation in estimation accuracy, or even failure, in underwater environments where non-Gaussian impulsive noise is predominant, thereby limiting the detection capabilities of underwater acoustic arrays. To address this issue, this study employs a method based on mixture correntropy, which maximizes the mixture correntropy of the residual fitting error matrix for subspace decomposition of the received data matrix, effectively filtering out impulsive noise. Considering the signal processing performance of correntropy and mixture correntropy depends on the selection of the kernel width, this article introduces a novel adaptive method for updating the kernel width. This method updates the kernel width in each iteration based on the residual fitting error value, setting the square of the kernel width to the sum of the squares of a preset kernel width and the residual fitting error modulus. This approach retains the simplicity and robustness of the maximum mixture correntropy criterion (MMCC) algorithm while enhancing the convergence rate and achieving a lower steady-state excess mean square error. Furthermore, this study applies the classical multiple signal classification (MUSIC) algorithm for DOA estimation. Finally, simulations and sea trials have substantiated the correctness and effectiveness of the method proposed in this article.

Joint detection and communication (JDC) systems can implement both functionalities simultaneously using the same hardware and software resources. This feature proves advantageous in reducing the size and power consumption of underwater vehicles. This paper develops a JDC system based on multi-input multi-output sonar by using orthogonal linear frequency modulation (OLFM) waveforms. Here, the proposed OLFM-based JDC system (OLFM-JDC) considers the detection functionality as the primary task. Therefore, OLFM-JDC exploits the mainlobe of transmit beam to detect targets and the sidelobes to communicate with the remote receivers. To enhance the information embedding capacity, the waveform diversity and the combination of index and phase modulations are utilized. Particularly, we propose a low-complexity two-step decoder to simplify the information decoding. Furthermore, the two-step decoder effectively utilizes multipath information to improve the performance of index decoding, and it can even outperform the maximum likelihood scheme when assessed within a simulated South China Sea acoustic channel. The numerical results demonstrate that OLFM-JDC achieves higher data rates and lower error rates compared to the JDC systems that only utilize phase modulation. Additionally, the simultaneous transmission of multiple waveforms facilitates target detection by utilizing the generalized high-resolution range profile synthesis technique. Performance analysis indicates that OLFM-JDC exhibits similar resolution performance to systems implementing a wideband waveform.

Favorable propagation (FP) and channel hardening (CH) are desired properties in massive multiple-input multiple-output (MIMO) systems. To date, these properties have primarily been analyzed for classical statistical channel models, or ray-based models with very specific angular parameters and distributions. This paper presents a thorough mathematical analysis of the asymptotic system behavior for ray-based channels with arbitrary ray distributions, and considers two types of antenna array structures at the cellular base station: a uniform linear array (ULA) and a uniform planar array (UPA). In addition to FP and channel hardening, we analyze the large system potential (LSP) which measures the asymptotic ratio of the expected power in the desired channel to the expected total interference power when both the antenna and user numbers grow. LSP is said to hold when this ratio converges to a positive constant. The results demonstrate that while FP is guaranteed in ray-based channels, CH may or may not occur depending on the nature of the model. Furthermore, we demonstrate that LSP will not normally hold as the expected interference power grows logarithmically for both ULAs and UPAs relative to the power in the desired channel as the system size increases. Nevertheless, we identify some fundamental and attractive properties of massive MIMO in this limiting regime.

Favorable propagation (FP) and channel hardening are desired properties in massive multiple-input and multiple-output (MIMO) systems, where nearly optimal performance is achieved with linear processing techniques, such as maximal-ratio combining. To date, these properties have primarily been analyzed for statistical channel models, or ray-based models with very specific angular parameters and distributions. This paper presents a thorough mathematical analysis of the asymptotic system behavior for ray-based channels with arbitrary ray distributions and a uniform linear array at the base station. In addition to FP and channel hardening, we analyze the large system potential (LSP) which measures the asymptotic signal-to-interference ratio when both the antenna and user numbers grow at an equal rate. The results demonstrate that while FP is guaranteed in ray-based channels, channel hardening may or may not occur depending on the nature of the model. Furthermore, we demonstrate that LSP will not normally hold as the interference power grows logarithmically relative to the signal as the array size increases. Nevertheless, we identify some fundamental and attractive properties of massive MIMO in this limiting regime.