Nathan Parrish

System Design Considerations for Undersea Networks: Link and Multiple Access Protocols

We address several inter-related aspects of underwater network design within the context of a cross-layer approach. We first highlight the impact of key characteristics of the acoustic propagation medium on the choice of link layer parameters; in turn, the consequences of these choices on design of a suitable MAC protocol and its performance are investigated. Specifically, the paper makes contributions on the following fronts: a) Based on accepted acoustic channel models, the pointto- point (link) capacity is numerically calculated, quantifying sensitivities to factors such as the sound speed profile, power spectral density of the (colored) additive background noise and the impact of boundary (surface) conditions for the acoustic channel; b) It provides an analysis of the Micromodem-like linklayer based on FH-FSK modulation; and finally c) it undertakes performance evaluation of a simple MAC protocol based on ALOHA with Random Backoff, that is shown to be particularly suitable for small underwater networks.

Symbol by symbol Doppler rate estimation for highly mobile underwater OFDM

We propose an OFDM receiver capable of estimating and correcting, on a symbol-by-symbol basis, the subcarrier dependent Doppler shifting due to the movement of source and receiver in an underwater acoustic network. We propose two methods of estimation: one of which is based upon the marginal maximum likelihood principle, and one of which is ad-hoc. We compare the performance of both estimators to the Cramer-Rao lower bound. We show through simulation that the proposed receiver design performs well for a source that is accelerating at 0.29 m/s2.

Rate-range for an FH-FSK acoustic modem

Signals transmitted through underwater channels experience attenuation due to dissipation of acoustic energy by spreading as well as by absorption. The path loss due to absorption is found to be highly dependent upon the frequency. Ambient noise, which also greatly affects accurate reception of the signal, is also highly dependent upon frequency. For these reasons, the received SNR cannot be assumed to be constant over wideband acoustic signaling schemes. In this paper we determine the (signaling) rate vs. range curves for a FH-FSK modem considering the frequency selective nature of signal attenuation in an acoustic medium.

Reliable early classification of time series

Early classification of time series is important in time-sensitive applications. An approach is presented for early classification using generative classifiers with the dual objectives of providing a class label as early as possible while guaranteeing with high probability that the early class matches the class that would be assigned to a longer time series. We give a specific algorithm for early quadratic discriminant analysis (QDA), and demonstrate that this classifier meets the requirement of reliable early classification.

Underwater Acoustic Communications Performance Modeling in Support of Ad Hoc Network Design

This paper discusses a methodology for predicting underwater acoustic communications performance using high fidelity acoustic time series simulation and acoustic modem processing emulation. Multiple source/receiver combinations can be simultaneously simulated, so that aspects of a complete underwater network can be studied. Here, the fundamental modeling and emulation capability will be described, with examples of the propagation modeling, time series simulation, and modem processing over multiple realizations of example communications channels. The results show the dependence of source and receiver location in the water column with respect to the sound speed profile on communications performance. The utility of such simulations for ad hoc network design in the presence of moving communications nodes will be discussed.

Bayesian transfer learning for noisy channels

We consider the problem of classifying a signal that is the output of a linear, time-invariant channel in the presence of additive noise, given two distinct sets of labeled data: one dataset of examples of the signals input to the channel, and a second dataset of example signals corrupted by the channel. We propose a distribution-based Bayesian quadratic discriminant analysis classifier that uses the input examples along with a model for the channel to form a prior for the likelihood of the output examples. Preliminary experiments with this proposed transfer BDA classifier show that it effectively uses both sets of data and is also robust to errors in channel modeling.

Robust classification of signal estimates given a channel model

In many signal processing applications, a signal to be classified has been corrupted by a channel and additive noise. A standard approach is to estimate the clean signal, then classify it. We consider two robust approaches that account for the estimation procedure. The first approach is an application of the MAP rule for noisy features, and the second is an approach for discriminative classifiers that treats that training points as random. An experiment confirms that the robust approaches offer performance gains.

Robust sequential classification of tracks

We present a robust probabilistic method to classify targets based on their tracks. As is customary in supervised learning problems, it is assumed that example tracks from various classes are available to train a classifier. We present an optimal but computationally intensive sequential solution, and show that a computationally feasible naive Bayes approximation works better than ignoring sequential information. We show how to take into account the uncertainty of the track, as quantified by the error covariance matrix from a Kalman tracker, using the recently proposed expected maximum likelihood rule coupled with a robust local Bayesian discriminant analysis classifier. In addition, we propose an expected maximum a posterior rule to take test sample uncertainty into account for classifiers that model the posterior, and use it to define a robust kernel classifier. Simulations with a Kalman tracker show significantly improved performance by taking into account the tracked state covariance.

Impact of bottom type on orthogonal frequency division multiplexing in underwater communications.

The impact of bottom sediment type in relation to acoustic communications via orthogonal frequency division multiplexing (OFDM) is shown via experimental results and simulation. Experimental data from Lake Washington, Seattle with a “silty clay” bottom show that the multipath delay spread is longer at 250 m than at 4 km. This results in better OFDM performance at the longer range. Similar results are shown via simulation using a channel model developed from Bellhop, a Gaussian Ray tracing tool [M. Porter, “Bellhop Gaussian beam/finite element beam code,” Available: http://oalib.hlsresearch.com/Rays/index.html (2007)]. Through simulation, results are also shown under similar conditions to the experiment but with varying bottom type. The results show that the performance of OFDM signaling is dependent on the bottom type as well as specific source/receiver geometry. [Work supported by NASA ESTO.]