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Predicting the channel quality for an underwater acoustic communication link is not a straightforward task. Previous approaches have focused on either physical observations of weather or engineered signal features, some of which require substantial processing to obtain. This work applies a convolutional neural network to the channel impulse responses, allowing the network to learn the features that are useful in predicting the channel quality. Results obtained are comparable or better than conventional supervised learning models, depending on the dataset. The universality of the learned features is also demonstrated by strong prediction performance when transferring from a more complex underwater acoustic channel to a simpler one. 

An underwater acoustic (UWA) channel model with high validity and re-usability is widely demanded. In this paper, we propose a variational auto-encoder (VAE)-based deep generative model which learns an abstract representation of the UWA channel impulse responses (CIRs) and can generate CIR samples with similar features. A customized training process is proposed to avoid the model collapse and being trapped in a gradient pit. The proposed deep generative model is validated using field experimental data sets. 

The underwater acoustic (UWA) channel is a complex and stochastic process with large spatial and temporal dynamics. This work studies the adaptation of the communication strategy to the channel dynamics. Specifically, a set of communication strategies are considered, including frequency shift keying (FSK), single-carrier communication, and multicarrier communication. Based on the channel condition, a reinforcement learning (RL) algorithm, the Depth Determined Strategy Gradient (DDPG) method along with a Gumbel-softmax scheme is employed for intelligent and adaptive switching among those communication strategies. The adaptive switching is performed on a transmission block-by-block basis, with the goal of maximizing a long-term system performance. The reward function is defined based on the energy efficiency and the spectral efficiency of the communication strategies. Simulation results reveal that the proposed method outperforms a random selection method in time-varying channels. 

Underwater acoustic communications provide promising solutions for remote and real-time aquatic exploration and monitoring. However, the underwater environment is rich in various kinds of interferences. Those interferences could severely degrade the acoustic communication performance. This work tackles interference cancellation in a single-carrier modulated communication system. Based on the Nyqusit sampling theorem, the interference is parameterized by a finite number of unknown parameters. The Page test is applied to detect the presence of an interfering waveform in the received signal. An iterative receiver is developed, which iteratively performs the interference estimation/cancellation and traditional receiver processing. The proposed receiver is evaluated when the communication waveform is interfered by the ice-cracking impulsive noise and the sonar signal collected from the Arctic. The data processing results reveal that the proposed receiver achieves considerable decoding performance improvement through the iterative interference estimation and cancellation. 

The propagation of acoustic waves under water is a highly complex and stochastic process. Such channel dynamics renders large performance variation in underwater acoustic (UWA) communications. Prediction of the UWA communication performance is critical for selection and adaptation of the communication strategies. This work explores the use of supervised learning for performance prediction in UWA communications. This work first quantifies the transmitter design, the UWA channel characteristics and the receiver design by numerical and categorical parameters. For a chosen performance metric (e.g., the bit error rate or the packet error rate), the performance prediction is cast individually into a numerical prediction problem and a classification problem. Using the data sets from two field experiments, the performance of typical supervised learning methods are examined. The data processing results reveal that some supervised learning methods can achieve fairly good numerical prediction or classification performance, and the discriminative models typically outperform the generative models. 

This work studies online learning-based trajectory planning for multiple autonomous underwater vehicles (AUVs) to estimate a water parameter field of interest in the under-ice environment. A centralized system is considered, where several fixed access points on the ice layer are introduced as gateways for communications between the AUVs and a remote data fusion center. We model the water parameter field of interest as a Gaussian process with unknown hyper-parameters. The AUV trajectories for sampling are determined on an epoch-by-epoch basis. At the end of each epoch, the access points relay the observed field samples from all the AUVs to the fusion center, which computes the posterior distribution of the field based on the Gaussian process regression and estimates the field hyper-parameters. The optimal trajectories of all the AUVs in the next epoch are determined to maximize a long-term reward that is defined based on the field uncertainty reduction and the AUV mobility cost, subject to the kinematics constraint, the communication constraint and the sensing area constraint. We formulate the adaptive trajectory planning problem as a Markov decision process (MDP). A reinforcement learning-based online learning algorithm is designed to determine the optimal AUV trajectories in a constrained continuous space. Simulation results show that the proposed learning-based trajectory planning algorithm has performance similar to a benchmark method that assumes perfect knowledge of the field hyper-parameters.