Underwater wireless networking and communications methodologies, including the routing algorithms, have been broadly studied these years. This paper designs an optical-acoustic hybrid network modeling approach, taking the characteristics of both the underwater optical and acoustic waves into account. Underwater nodes are divided into two types, namely intra-cluster and cluster head nodes, in which energy consumption and time delay in underwater optical and acoustic links are both mathematically modeled and TDMA MAC protocol is reasonably applied in acoustic and optical links. Based on these preliminaries, a cooperative routing protocol is proposed using the Q-learning technique, where the forwarding actions are determined according to received rewards and experiential knowledge. To be specific, sender nodes will evaluate the effects of all possible forwarding actions on the network and routing performances before selecting the receiver nodes. It is shown by simulation experiment results that this routing protocol outperforms three relevant noted underwater routing algorithms in terms of network connectivity rate, lifetime, energy efficiency, energy consumption balance, packet-loss rate, and end-to-end delay.

Underwater optical-acoustic hybrid networking can complement the advantages of both underwater wireless optical and acoustic communications, which holds great development prospects. This paper designs a novel and feasible underwater optical-acoustic hybrid sensor network topology, which fully considers the differences between underwater sound and light communication systems. Following this network topology, an adaptive and green power control aided routing protocol is proposed, mainly including two stages: the power control and the packet forwarding. These two procedures employ potential game theory and ${Q}$ -learning technique respectively, making each node adjust its transmitted power and forward packets in an adaptive and distributed manner with energy efficiency, packet advancement, and network connectivity as the optimization goals. According to the simulation results, the proposed power control and packet forwarding algorithms are of reasonable complexity and convergence speed, and adding the power control before the packet forwarding can achieve better routing performance. Compared with relevant routing protocols, the designed protocol has significant advantages in energy efficiency, end-to-end delay, and network lifetime.

This paper addresses the channel estimation problem for Orthogonal time frequency space (OTFS) systems in the presence of fractional Doppler. Channel estimation with fractional Doppler can be considered as an off-grid sparse signal recovery problem, where the virtual sampling grid is introduced in the delay-Doppler domain. First-order linear approximation as a conventional approach to estimate fractional Doppler and sparse signal in OTFS systems. However, linear approximation approach may incur considerable modeling errors, since the finite sampling grid may be insufficiently refined in the OTFS systems. Furthermore, this error will deteriorate recovery performance. To solve this problem, we propose an efficient sparse Bayesian learning (SBL) method to jointly estimate the fractional Doppler and sparse channel vectors. Specifically, we reformulate the input-output relationship for fractional Doppler, and further present an off-grid channel estimation model. Then, an in-exact block majorization-minimization (MM) algorithm is adopted to iteratively update the associated parameters under the SBL framework. Fractional Doppler can be iteratively refined to eliminate the off-grid gap using the gradient descent method. The proposed scheme presents an exact channel estimation model that avoids any approximation operation, so that it effectively alleviates the model error. Simulation results show that the proposed channel estimation method significantly outperforms state-of-the-art methods

This letter proposes a learned denoising-based sparse adaptive channel estimation method in the delay-Doppler domain for time-varying underwater acoustic (UWA) channels in an orthogonal time-frequency space (OTFS) system. We first propose a symbol-wise adaptive channel estimation method for the OTFS system. By leveraging the sparsity characteristic of the channels, we employ the improved proportionate normalized least mean squares (IPNLMS) algorithm. Based on the characteristic that the channel in the delay-Doppler domain is invariant, the multiple estimates obtained from the adaptive filter could be regarded as multiple noisy images derived from the same clean image. A neural network called FastDVDNet, commonly used in video denoising, is utilized to exploit the correlation among the multiple images. The simulation results demonstrate that the proposed denoising strategies significantly enhance the estimation performance, thereby achieving superior channel estimation results.

Orthogonal time-frequency-space (OTFS) modulation has shown significant error performance advantages over orthogonal frequency division multiplexing (OFDM) in time-varying channels. However, the symbols in OTFS frames still suffer from inter-Doppler interference (IDI) and inter-symbol interference (ISI). In this letter, we propose a two dimensional (2D) adaptive multichannel decision feedback equalizer (DFE) to combat the interference. The proposed receiver constructs the input signal and feedback signal of the DFE based on the characteristic of OTFS modulation. Meanwhile, by exploiting the sparsity feature of the channels in delay-Doppler domain, the iterative improved proportionate normalized least mean squares (IPNLMS) algorithm is employed to achieve fast convergence and fast tracking. The simulation results show that the proposed equalizer could achieve satisfied BER performance with lower complexity.

With the development of the underwater acoustic (UWA) adaptive communication system, energy-efficient transmission has become a critical topic in underwater acoustic (UWA) communications. Due to the unique characteristics of the underwater environment, the transmitter node will almost always have outdated channel state information (CSI), which results in low energy efficiency. In this paper, we take full advantage of bidirectional links and propose an adaptive modulation and coding (AMC) scheme that aims to maximize the long-term energy efficiency of a single link by jointly scheduling the coding rate, modulation order, and transmission power. Considering the complexity characteristics of UWA channels, we proposed a bit error ratio (BER) estimation method based on deep neural networks (DNN). The proposed network could realize channel estimation, feature extraction, and BER estimation by using a fixed pilot of the feedback link. Then, we design a channel classification method based on the estimated BERs of the modulation and coding scheme (MCS) and further model the UWA channels as a finite-state Markov chain (FSMC) with an unknown transition probability. Thus, we formulate the AMC problem as a Markov Decision Process (MDP) and solve it through a reinforcement learning framework. Considering the large state-action pairs, a double deep Q-network (DDQN) based scheme is proposed. Simulation results demonstrate that the proposed AMC scheme outperforms the fixed MCS with a perfect channel information state, and achieves near-optimal energy efficiency.