› › Adaptive Channel Equalization In The Time Varying Underwater Acoustic Channel Performance Characterization And Robust Equalizers

Adaptive Channel Equalization in the Time-Varying Underwater Acoustic Channel: Performance Characterization and Robust Equalizers

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Page : 23 pages

File Size : 46,1 MB

Release : 2004

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Channel-estimate-based equalizers are adaptive coherent equalizers for which observations of the received signal are used to estimate channel parameters and these estimates are used to calculate the equalizer filter weights. Traditional channel-estimate- based equalizers calculate filter weights assuming that the estimates of the channel parameters are perfect. This work presents a common framework for evaluating both the performance of channel-estimate- based equalizers when the channel estimates are perfect (i.e. the minimal achievable error of the equalizer) and the degradation in performance of these equalizers due to errors in the channel estimates (i.e. the excess error). For the three type of equalizers considered (DFE Linear MMSE and Passive Time Reversal) the expressions for minimal achievable error take the form of the results from classical estimation theory for estimation error achieved by MMSE and matched filter estimators. These expressions are interpreted to give insights into the characteristics of "good" and "bad" channels. For the case when the channel estimates are MMSE estimates of the channel-impulse response, the excess error is shown to be proportional to the 2-norm of the calculated feedforward filter weight vector of the equalizer. This result is analogous to the "white noise gain" result characterizing the sensitivity of adaptive array processors to mismatch. This result is used to evaluate the relative sensitivity of all three types of equalizers to environmental mismatch. The analytic predictions of equalizer performance are compared with observed performance using data from several field experiments in different underwater acoustic environments. The expressions for minimal achievable error and excess error give insights into potential methods of improving the robustness to channel mismatch of adaptive equalizers such as the DFE. Several of these methods are implemented and evaluated.



Analysis of and Techniques for Adaptive Equalization for Underwater Acoustic Communication

Author : Ballard Justin Smith Blair

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Page : 215 pages

File Size : 35,51 MB

Release : 2011

Category : Acoustic models

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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.





Iterative Equalization and Decoding Applied to Underwater Acoustic Communication

Author : James F. Sifferlen

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Page : 134 pages

File Size : 38,16 MB

Release : 2008

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This dissertation focuses on data communication over shallow, long-range underwater acoustic (UWA) channels, which are characterized by relatively long, time-varying impulse responses and acute sensitivity to Doppler effects. The latter is a result of the slow speed of sound in water, while the former is a consequence of the waveguide nature of shallow, long-range UWA channels. A succession of novel receiver algorithms are developed which recover digital information transmitted across such channels. The first considers the case of a single, fixed source transducer transmitting information to a single, fixed receive hydrophone. The second extends the first to allow nontrivial source motion, for instance, that of an autonomous undersea vehicle. The third extends the second to allow processing of data received on an array of hydrophones. The algorithms employ iterative detection. Iterative processing is creating a paradigm shift in digital communication, made possible by ever-increasing computational capabilities. There are two major components to the algorithms: an equalizer and a decoder. The main focus of this dissertation is the former which, because of the features of the UWA channel, and since the channel is not assumed known a priori at the receiver, entails adaptive resampling to correct for Doppler distortion, adaptive filtering to estimate the time-varying channel, and adaptive equalization to compensate for intersymbol interference produced by the long channel impulse responses. While decoding is performed using standard methods, its role is nonetheless crucial to the overall functioning of the algorithms. In fact, they rely on the iterative exchange of information between equalizer and decoder, and the improvement of that information with each iteration. Successful performance of the algorithms is demonstrated using data from at-sea experiments.



Direct-form Adaptive Equalization for Underwater Acoustic Communication

Author : Atulya Yellepeddi

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Page : 143 pages

File Size : 35,67 MB

Release : 2012

Category : Mathematical models

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Adaptive equalization is an important aspect of communication systems in various environments. It is particularly important in underwater acoustic communication systems, as the channel has a long delay spread and is subject to the effects of time- varying multipath fading and Doppler spreading. The design of the adaptation algorithm has a profound influence on the performance of the system. In this thesis, we explore this aspect of the system. The emphasis of the work presented is on applying concepts from inference and decision theory and information theory to provide an approach to deriving and analyzing adaptation algorithms. Limited work has been done so far on rigorously devising adaptation algorithms to suit a particular situation, and the aim of this thesis is to concretize such efforts and possibly to provide a mathematical basis for expanding it to other applications. We derive an algorithm for the adaptation of the coefficients of an equalizer when the receiver has limited or no information about the transmitted symbols, which we term the Soft-Decision Directed Recursive Least Squares algorithm. We will demonstrate connections between the Expectation-Maximization (EM) algorithm and the Recursive Least Squares algorithm, and show how to derive a computationally efficient, purely recursive algorithm from the optimal EM algorithm. Then, we use our understanding of Markov processes to analyze the performance of the RLS algorithm in hard-decision directed mode, as well as of the Soft-Decision Directed RLS algorithm. We demonstrate scenarios in which the adaptation procedures fail catastrophically, and discuss why this happens. The lessons from the analysis guide us on the choice of models for the adaptation procedure. We then demonstrate how to use the algorithm derived in a practical system for underwater communication using turbo equalization. As the algorithm naturally incorporates soft information into the adaptation process, it becomes easy to fit it into a turbo equalization framework. We thus provide an instance of how to use the information of a turbo equalizer in an adaptation procedure, which has not been very well explored in the past. Experimental data is used to prove the value of the algorithm in a practical context.





Statistical Characterization of a Class of Underwater Acoustic Communication Channels

Author : Parastoo Qarabaqi

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Page : 91 pages

File Size : 40,76 MB

Release : 2014

Category : Underwater acoustic telemetry

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Acoustic channel models provide a tool for predicting the performance of underwater communication systems prior to deployment, and are thus essential for system design. In this dissertation, we offer a statistical channel model, which incorporates physical laws of acoustic propagation (frequency-dependent attenuation, bottom-surface reflections) as well as the effects of inevitable random local displacements. We focus on random displacements on two scales: small-scale effects, that involve distances on the order of a few wavelengths, and large-scale effects, that involve many wavelengths. Small-scale effects include scattering and motion-induced Doppler shifting, and are responsible for fast variations of the instantaneous channel response; while large-scale effects describe the location uncertainty and changing environmental conditions, and affect the locally-averaged received power. We model each propagation path by a large-scale gain and micro-multipath components that cumulatively result in a complex Gaussian distortion. Random surface motion and transducer displacement introduce additional variation whose temporal correlation is described by Bessel-type functions. The total power, or the gain contained in the channel, averaged over small-scale, is modeled as log-normally distributed. The models are validated using real data obtained from four experiments. Specifically, experimental data are used to assess the distribution and the auto-correlation functions of the large-scale transmission loss and the short-term path gains. While the former indicates a log-normal distribution with an exponentially decaying auto-correlation, the latter indicates a conditional Ricean distribution with Bessel-type auto-correlation. Based on the proposed model, we design a channel simulator which we employ to generate a time-varying channel whose statistical characteristics match with those of a real underwater channel. The simulated channel is applied to convey an OFDM signal to coherent and differentially coherent detectors, and the MSE performance of the experimental and simulated systems are shown to be similar. Finally, we investigate the feasibility of adaptive power control using an experimental data set as well as theoretically. Based on the observed time-correlation properties of the large-scale channel gain, linear power prediction is employed and achievable power savings are obtained analytically (assuming a log-normal gain distribution) and experimentally. The results indicate that substantial power savings are possible over extended periods of time.



Combined Spatial Diversity and Time Equalization for Broadband Multiple Channel Underwater Acoustic Communications

Author : Violeta Skoro Kaskarovska

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Page : 136 pages

File Size : 44,40 MB

Release : 2015

Category : Adaptive signal processing

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High data rate acoustic communications become feasible with the use of communication systems that operate at high frequency. The high frequency acoustic transmission in shallow water endures severe distortion as a result of the extensive intersymbol interference and Doppler shift, caused by the time variable multipath nature of the channel. In this research a Single Input Multiple Output (SIMO) acoustic communication system is developed to improve the reliability of the high data rate communications at short range in the shallow water acoustic channel. The proposed SIMO communication system operates at very high frequency and combines spatial diversity and decision feedback equalizer in a multilevel adaptive configuration. The first configuration performs selective combining on the equalized signals from multiple receivers and generates quality feedback parameter for the next level of combining. The second configuration implements a form of turbo equalization to evaluate the individual receivers using the feedback parameters as decision symbols. The improved signals from individual receivers are used in the next iteration of selective combining. Multiple iterations are used to achieve optimal estimate of the received signal. The multilevel adaptive configuration is evaluated on experimental and simulated data using SIMO system with three, four and five receivers. The simulation channel model developed for this research is based on experimental channel and Rician fading channel model. The performance of the channel is evaluated in terms of Bit Error Rate (BER) and Signal-to-Noise-and-Interference Ratio (SNIR). Using experimental data with non-zero BER, multilevel adaptive spatial diversity can achieve BER of 0 % and SNIR gain of 3 dB. The simulation results show that the average BER and SNIR after multilevel combining improve dramatically compared to the single receiver, even in case of extremely high BER of individual received signals. The results demonstrate the ability of the proposed multilevel adaptive combining approach to significantly improve the performance of the shallow water acoustic channel, while preserving the same transmission power and channel bandwidth.



Spectrally Efficient Underwater Acoustic Communications

Author : Andreja Radošević

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Page : 186 pages

File Size : 46,11 MB

Release : 2012

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ISBN : 9781267433497

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In this dissertation, we consider design aspects of spectrally efficient underwater acoustic (UWA) communications. In particular, we first focus on statistical characterization and capacity evaluation of shallow water acoustic communications channels. Wideband single-carrier and multi-carrier probe signals are employed during the Kauai Acoustic Communications MURI 2008 (KAM08) and 2011 (KAM11) experiments, to measure the time-varying channel response, and to estimate its statistical properties and capacity that play an important role in the design of spectrally efficient communication systems. Besides the capacity analysis for unconstrained inputs, we determine new bounds on the achievable information rate for discrete-time Gaussian channels with inter-symbol interference and independent and uniformly distributed channel input symbols drawn from finite-order modulation alphabets. Specifically, we derived new bounds on the achievable rates for sparse channels with long memory. Furthermore, we explore design aspects of adaptive modulation based on orthogonal frequency division multiplexing (OFDM) for UWA communications, and study its performance using real-time at-sea experiments. Lastly, we investigate a channel estimation (CE) method for improving the spectral efficiency of UWA communications. Specifically, we determine the performance of a selective decision directed (DD) CE method for UWA OFDM-based communications.



Adaptive Feature Representation to Improve, Interpret and Accelerate Channel Estimation and Prediction for Shallow Water Acoustic Environments

Author : Ryan A. McCarthy (PhD)

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Page : 0 pages

File Size : 45,76 MB

Release : 2021

Category : Underwater acoustics

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In my doctoral dissertation I investigate new approaches to real-time channel estimation of underwater acoustic communications that complement existing estimation techniques. Modified sparse optimization algorithms have been used to improve channel estimation with some success. This work aims to improve these algorithms by applying pattern recognition through adaptive signal processing and machine learning to accelerate estimation time. Specifically, it investigates a model-agnostic geometric feature morphology based on braid theory to interpret diverse channel phenomena. The computational goal is to detect, separate and interpret multipath features in the channel delay spread across time, frequency, and varying degrees of channel sparsity. The main contribution of the thesis is development of braids feature representations and related channel tracking and learning algorithms to track salient bands of multipath activity. We develop robust signal processing and braided feature engineering approaches that evolve dynamically to the fluctuating channel multipath activity. To test the hypothesis that braids can track and adapt to diverse activity developing within the channel, simulated shallow water environments created through the well-known BELLHOP model and data from the SPACE08 field experiment are examined. Several simulated shallow water environments are examined with additive white Gaussian noise and varying degrees of activity to evaluate the performance of braiding and machine learning for shallow water acoustic channel estimation and interpretation. Performance is evaluated through visual confirmation and ground truths are provided by BELLHOP's outputs (e.g. eigenrays, arrivals, etc.). Results show that braids can evolve to capture dynamically changing multipath scattering activity in the shallow water acoustic channel. Furthermore, we demonstrate that leveraging braid feature representations with acoustic physics propagation models can successfully predict the number of reflectors in active channel multipath. We also demonstrate the significance of braid manifold representation in improving the computational speed for channel estimation. On average, this technique has improved estimation speed by ~.02 seconds as compared to the existing estimation techniques. These results suggest that braids can be used for useful pattern recognition to bridge the gap between purely statistical data analysis and physics-driven interpretation of the ocean acoustics that create the multipath channel delay spread. Beyond underwater acoustics, these feature learning techniques are broadly applicable to any paradigms where spectral features may evolve and intersect.