In underwater acoustic (UWA) adaptive communication system, due to time-varying channel, the transmitter often has outdated channel state information (CSI), which results in low efficiency. UWA channels are much more difficult to estimate and predict than terrestrial wireless channels, given the more severe multipath environments with varying propagation speeds in different locations, non-linear propagation paths, several-order higher propagation latency, mobile transceiver and obstacles in the sea, etc. To handle the complexity, this paper proposes an efficient online CSI prediction model for UWA CSI prediction considering the complicated correlationship of UWA channels in both the time and frequency domains. This paper designs a learning model called CsiPreNet, which is an integration of a one-dimensional convolutional neural network (CNN) and a long short term memory (LSTM) network. The performance is compared with the widely used recursive least squares (RLS) predictor, the approximate linear dependency recursive kernel least-squares (ALD-KRLS), and two common conventional deep neural networks (DNN) predictors, i.e., back propagation neural network (BPNN) and LSTM network using the measured data recorded in the South China Sea. To validate the efficacy of prediction, we investigate the prediction of CSI in simulated downlink UWA orthogonal frequency division multiple access (OFDMA) systems. Specifically, the measured UWA channel is used in the OFDMA system. A joint subcarrier-bit-power adaptive allocation scheme is used for resource allocation. To further improve the performance, we develop an offline-online prediction scheme, enabling the prediction results to be more stable. Simulation and experimental results show that the CsiPreNet has superior performance than the existing solutions, thanks to its capability in capturing both the temporal and frequency correlation of the UWA CSIs.

Use of magnetic fields for underwater wireless communication recently has been found very promising due to its advantages of being low power, low cost, no multipath, no Doppler effect and instant communication. Keeping in view the importance and the potential of magnetic field communication for underwater wireless sensor networks, this paper presents a review of the fundamental concepts and theory of magnetic field communication by identifying the main blocks, describing the limitation of each block and mentioning the advancements being done. The three fundamental blocks identified are: 1) the transmit and receive coils, 2) matching network, and 3) channel characteristics of magnetic fields. The first block explains how a single dimensional coil is used to establish a directional communication link between a transmitter & receiver and then how the directionality problem is solved with the help of multi-dimensional coils and meta-material coils. Further, the matching network, explains the difference between the induction and resonance phenomena and their effect on the range and capacity. In the third block, the factors affecting the magnetic fields are mentioned that are critical for underwater wireless communication with magnetic fields. Finally, the paper introduces the recent advancements at physical, MAC and network layers.

Underwater wireless sensor networks (UWSNs) have become highly efficient in performing different operations in oceanic environments. Compared to terrestrial wireless sensor networks (TWSNs), MAC and routing protocols in UWSNs are prone to low bandwidth, low throughput, high energy consumption, and high propagation delay. UWSNs are located remotely and do not need to operate with any human involvement. In UWSNs, the majority of sensor batteries have limited energy and very difficult to replace. The uneven use of energy resources is one of the main problems for UWSNs, which reduce the lifetime of the network. Therefore, an energy-efficient MAC and routing techniques are required to address the aforementioned challenges. Several important research projects have been tried to realize this objective by designing energy-efficient MAC and routing protocols to improve efficient data packet routing from Tx anchor node to sensor Rx node. In this article, we concentrate on discussing about different energy-efficient MAC and routing protocols which are presently accessible for UWSNs, categorize both MAC and routing protocols with a new taxonomy, as well as provide a comparative discussion. Finally, we conclude by presenting various current problems and research difficulties for future research.

To improve the throughput of underwater acoustic (UWA) networking, the In-band full-duplex (IBFD) communication is one of the most vital pieces of research. The major drawback of IBFD-UWA communication is Self-Interference (SI). This paper presents a digital SI cancellation algorithm for asynchronous IBFD-UWA communication system. We focus on two issues: one is asynchronous communication dissimilar to IBFD radio communication, the other is nonlinear distortion caused by power amplifier (PA). First, we discuss asynchronous IBFD-UWA signal model with the nonlinear distortion of PA. Then, we design a scheme for asynchronous IBFD-UWA communication utilizing the non-overlapping region between SI and intended signal to estimate the nonlinear SI channel. To cancel the nonlinear distortion caused by PA, we propose an Over-Parameterization based Recursive Least Squares (RLS) algorithm (OPRLS) to estimate the nonlinear SI channel. Furthermore, we present the OPRLS with a sparse constraint to estimate the SI channel, which reduces the requirement of the length of the non-overlapping region. Finally, we verify our concept through simulation and the pool experiment. Results demonstrate that the proposed digital SI cancellation scheme can cancel SI efficiently.

This paper investigates the performance of vertical underwater wireless optical communication (UWOC) systems in the presence of air bubbles and temperature gradients. We consider a generalized UWOC channel model that contains $N$ layers, each having the same distribution but with different parameters to consider the vertically inhomogeneous nature of the underwater environment. To capture the effects of air bubbles and temperature gradients on channel statistics, we model each layer with a mixture exponential-generalized Gamma distribution. A closed-form probability density function (PDF) is presented for the end-to-end signal-to-noise ratio. Based on the PDF, we derive the average bit-error-rate (BER) and the ergodic capacity. Moreover, we provide asymptotic BER and ergodic capacity results in simple forms. The performance and behavior of vertical UWOC systems are thoroughly analyzed, and all the derived expressions are verified via Monte Carlo simulations.

Underwater acoustic (UWA) channel prediction technology, as an important topic in UWA communication, has played an important role in UWA adaptive communication network and underwater target perception. Although many significant advancements have been achieved in underwater acoustic channel prediction over the years, a comprehensive summary and introduction is still lacking. As the first comprehensive overview of UWA channel prediction, this paper introduces past works and algorithm implementation methods of channel prediction from the perspective of linear, kernel-based, and deep learning approaches. Importantly, based on available at-sea experiment datasets, this paper compares the performance of current primary UWA channel prediction algorithms under a unified system framework, providing researchers with a comprehensive and objective understanding of UWA channel prediction. Finally, it discusses the directions and challenges for future research. The survey finds that linear prediction algorithms are the most widely applied, and deep learning, as the most advanced type of algorithm, has moved this field into a new stage. The experimental results show that the linear algorithms have the lowest computational complexity, and when the training samples are sufficient, deep learning algorithms have the best prediction performance.