This article investigates a robust receiver scheme for a single carrier, multiple-input–multiple-output (MIMO) underwater acoustic (UWA) communications, which uses the sparse Bayesian learning algorithm for iterative channel estimation embedded in Turbo equalization (TEQ). We derive a block-wise sparse Bayesian learning framework modeling the spatial correlation of the MIMO UWA channels, where a more robust expectation–maximization algorithm is proposed for updating the joint estimates of channel impulse response, residual noise, and channel covariance matrix. By exploiting the spatially correlated sparsity of MIMO UWA channels and the second-order a priori channel statistics from the training sequence, the proposed Bayesian channel estimator enjoys not only relatively low complexity but also more stable control of the hyperparameters that determine the channel sparsity and recovery accuracy. Moreover, this article proposes a low complexity space-time soft decision feedback equalizer (ST-SDFE) with successive soft interference cancellation. Evaluated by the undersea 2008 Surface Processes and Acoustic Communications Experiment, the improved sparse Bayesian learning channel estimation algorithm outperforms the conventional Bayesian algorithms in terms of the robustness and complexity, while enjoying better estimation accuracy than the orthogonal matching pursuit and the improved proportionate normalized least mean squares algorithms. We have also verified that the proposed ST-SDFE TEQ significantly outperforms the low-complexity minimum mean square error TEQ in terms of the bit error rate and error propagation.
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Supervised Learning for Performance Prediction in Underwater Acoustic Communications
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.
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- Award ID(s):
- 1651135
- NSF-PAR ID:
- 10314540
- Date Published:
- Journal Name:
- Global Oceans 2020: Singapore – U.S. Gulf Coast
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Underwater acoustic (UWA) communications have attracted a lot of interest in recent years motivated by a wide range of applications including offshore oil field exploration and monitoring, oceanographic data collection, environmental monitoring, disaster prevention, and port security. Different signaling solutions have been developed to date including non‐coherent communications, phase coherent systems, multi‐input and multi‐output solutions, time‐reversal‐based communication systems, and multi‐carrier transmission approaches. This paper deviates from the traditional approaches to UWA communications and develops a scheme that exploits biomimetic signals. In the proposed scheme, a transmitter maps the information bits to the parameters of a biomimetic signal, which is transmitted over the channel. The receiver estimates the parameters of the received signal and demaps them back to bits to estimate the message. As exemplary biomimetic signals, analytical signal models with nonlinear instantaneous frequency are developed that match mammal sound signatures in the time‐frequency plane are developed. Suitable receiver structures as well as performance analysis are provided for the proposed transmission scheme, and some results using data recorded during the Kauai Acomms MURI 2011 UWA communications experiment are presented. Copyright © 2016 John Wiley & Sons, Ltd.
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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.more » « less
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Adaptive Switching for Multimodal Underwater Acoustic Communications Based on Reinforcement LearningThe 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.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/IEEECONF38699.2020.9389191
