Analysis of and techniques for adaptive equalization for underwater acoustic communication

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution September 2011Underwater wireless communication is quickly becoming a necessity for applications in ocean science, defense, and homeland security. Acoustics remains the only practical means of accomplishing long-range communication in the ocean. The acoustic communication channel is fraught with difficulties including limited available bandwidth, long delay-spread, time-variability, and Doppler spreading. These difficulties reduce the reliability of the communication system and make high data-rate communication challenging. Adaptive decision feedback equalization is a common method to compensate for distortions introduced by the underwater acoustic channel. Limited work has been done thus far to introduce the physics of the underwater channel into improving and better understanding the operation of a decision feedback equalizer. This thesis examines how to use physical models to improve the reliability and reduce the computational complexity of the decision feedback equalizer. The specific topics covered by this work are: how to handle channel estimation errors for the time varying channel, how to use angular constraints imposed by the environment into an array receiver, what happens when there is a mismatch between the true channel order and the estimated channel order, and why there is a performance difference between the direct adaptation and channel estimation based methods for computing the equalizer coefficients. For each of these topics, algorithms are provided that help create a more robust equalizer with lower computational complexity for the underwater channel.This work would not have been possible without support from the O ce of Naval Research, through a Special Research Award in Acoustics Graduate Fellowship (ONR Grant #N00014-09-1-0540), with additional support from ONR Grant #N00014-05- 10085 and ONR Grant #N00014-07-10184

Analysis of and techniques for adaptive equalization for underwater acoustic communication

Thesis (Ph. D.)--Joint Program in Oceanography/Applied Ocean Science and Engineering (Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science; and the Woods Hole Oceanographic Institution), 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 203-215).Underwater wireless communication is quickly becoming a necessity for applications in ocean science, defense, and homeland security. Acoustics remains the only practical means of accomplishing long-range communication in the ocean. The acoustic communication channel is fraught with difficulties including limited available bandwidth, long delay-spread, time-variability, and Doppler spreading. These difficulties reduce the reliability of the communication system and make high data-rate communication challenging. Adaptive decision feedback equalization is a common method to compensate for distortions introduced by the underwater acoustic channel. Limited work has been done thus far to introduce the physics of the underwater channel into improving and better understanding the operation of a decision feedback equalizer. This thesis examines how to use physical models to improve the reliability and reduce the computational complexity of the decision feedback equalizer. The specific topics covered by this work are: how to handle channel estimation errors for the time varying channel, how to use angular constraints imposed by the environment into an array receiver, what happens when there is a mismatch between the true channel order and the estimated channel order, and why there is a performance difference between the direct adaptation and channel estimation based methods for computing the equalizer coefficients. For each of these topics, algorithms are provided that help create a more robust equalizer with lower computational complexity for the underwater channel.by Ballard J. S. Blair.Ph.D

Enabling Technology in Optical Fiber Communications: From Device, System to Networking

This book explores the enabling technology in optical fiber communications. It focuses on the state-of-the-art advances from fundamental theories, devices, and subsystems to networking applications as well as future perspectives of optical fiber communications. The topics cover include integrated photonics, fiber optics, fiber and free-space optical communications, and optical networking

Deep Learning Methods for Nonlinearity Mitigation in Coherent Fiber-Optic Communication Links

Nowadays, the demand for telecommunication services is rapidly growing. To meet this everincreasing connectivity demand telecommunication industry needs to maintain the exponential growth of capacity supply. One of the central efforts in this initiative is directed towards coherent fiber-optic communication systems, the backbone of modern telecommunication infrastructure. Nonlinear distortions, i.e., the ones dependent on the transmitted signal, are widely considered to be one of the major limiting factors of these systems. When mitigating these distortions, we can’t rely on the pre-recorded information about channel properties, which is often missing or incorrect, and, therefore, have to resort to adaptive mitigation techniques, learning the link properties by themselves. Unfortunately, the existing practical approaches are suboptimal: they assume weak nonlinear distortion and propose its compensation via a cascade of separately trained sub-optimal algorithms. Deep learning, a subclass of machine learning very popular nowadays, proposes a way to address these problems. First, deep learning solutions can approximate well an arbitrary nonlinear function without making any prior assumptions about it. Second, deep learning solutions can effectively optimize a cluster of single-purpose algorithms, which leads them to a global performance optimum. In this thesis, two deep-learning solutions for nonlinearity mitigation in high-baudrate coherent fiber-optic communication links are proposed. The first one is the data augmentation technique for improving the training of supervised-learned algorithms for the compensation of nonlinear distortion. Data augmentation encircles a set of approaches for enhancing the size and the quality of training datasets so that they can lead us to better supervised learned models. This thesis shows that specially designed data augmentation techniques can be a very efficient tool for the development of powerful supervised-learned nonlinearity compensation algorithms. In various testcases studied both numerically and experimentally, the suggested augmentation is shown to lead to the reduction of up to 6× in the size of the dataset required to achieve the desired performance and a nearly 2× reduction in the training complexity of a nonlinearity compensation algorithm. The proposed approach is generic and can be applied to enhance a multitude of supervised-learned nonlinearity compensation techniques. The second one is the end-to-end learning procedure enabling optimization of the joint probabilistic and geometric shaping of symbol sequences. In a general end-to-end learning approach, the whole system is implemented as a single trainable NN from bits-in to bits-out. The novelty of the proposed approach is in using cost-effective channel model based on the perturbation theory and the refined symbol probabilities training procedure. The learned constellation shaping demonstrates a considerable mutual information gains in single-channel 64 GBd transmission through both single-span 170 km and multi-span 30x80 km single-mode fiber links. The suggested end-to-end learning procedure is applicable to an arbitrary coherent fiber-optic communication link

Advanced Technique and Future Perspective for Next Generation Optical Fiber Communications

Optical fiber communication industry has gained unprecedented opportunities and achieved rapid progress in recent years. However, with the increase of data transmission volume and the enhancement of transmission demand, the optical communication field still needs to be upgraded to better meet the challenges in the future development. Artificial intelligence technology in optical communication and optical network is still in its infancy, but the existing achievements show great application potential. In the future, with the further development of artificial intelligence technology, AI algorithms combining channel characteristics and physical properties will shine in optical communication. This reprint introduces some recent advances in optical fiber communication and optical network, and provides alternative directions for the development of the next generation optical fiber communication technology

Wavelet Theory

The wavelet is a powerful mathematical tool that plays an important role in science and technology. This book looks at some of the most creative and popular applications of wavelets including biomedical signal processing, image processing, communication signal processing, Internet of Things (IoT), acoustical signal processing, financial market data analysis, energy and power management, and COVID-19 pandemic measurements and calculations. The editor’s personal interest is the application of wavelet transform to identify time domain changes on signals and corresponding frequency components and in improving power amplifier behavior

Path Planning and Performance Evaluation Strategies for Marine Robotic Systems

The field of marine robotics offers many new capabilities for completing dangerous missions such as deep-sea exploration and underwater demining. The harshness of marine environments, however, means that without effective onboard decision-making, vehicle loss or mission failure are likely. Thus, to enable more autonomous operation while building trust that these systems will perform as expected, this thesis develops improved path planning and testing strategies for two different types of marine robotic platforms. The first portion of the research focuses on improved environmental data collection with an autonomous underwater vehicle (AUV). Gaussian process-based modeling is combined with informative path planning to explore an environment, while preferentially collecting data in regions of interest that exhibit extreme sensory measurements. The performance of this adaptive data sampling framework with a torpedo-style AUV is studied in both simulation and field experiments. Results show that the proposed methodology is able to be fielded on an operational platform and collect measurements in regions of interest without sacrificing overall model fidelity of the full sampling area. The second portion of the research then focuses on autonomous surface vessel (ASV) navigation that must comply with international collision avoidance standards and basic ship handling principles. The approach introduces a novel quantification of good seamanship that is used within an ASV path planner to minimize the collision risk with other vessels. This approach generalizes well to both single-vessel and multi-vessel encounters by avoiding rule-based conditions. The performance of this ASV planning strategy is evaluated in simulation against other baseline planners, and the results of on-water testing with a 29-ft ASV demonstrate that the approach is scalable to real systems. Beyond developing improved path planning frameworks, this research also explores methods for improved testing and evaluation of black-box autonomous systems. Statistical learning techniques such as adaptive scenario generation and unsupervised clustering are used to extract the failure modes of the autonomy from large-scale simulation datasets. Subsequently, changes in these failure modes are tracked in a novel form of performance-based regression testing. The effectiveness of this testing framework is demonstrated on the aforementioned ASV planner by discovering several types of unexpected failures