Statistical bit-error modeling of shallow water acoustic communication links

Underwater network simulation and performance analysis require accurate packet error models. The packet error probability depends on the packet length and the temporal distribution of bit errors. We analyze error traces from the SPACE’08 experiment and show that clustering of errors occurs at several time-scales. We propose a two-part statistical error model consisting of a generalized Pareto fractal renewal parent process that drives Bernoulli daughter processes with generalized extreme value distributed lifetimes. We present an algorithm to simulate communication errors using this error process model and show that the simulated packet loss probability accurately matches experimental observations.

[1]  U. Fano Ionization Yield of Radiations. II. The Fluctuations of the Number of Ions , 1947 .

[2]  Jay M. Berger,et al.  A New Model for Error Clustering in Telephone Circuits , 1963, IBM J. Res. Dev..

[3]  Benoit B. Mandelbrot,et al.  Self-Similar Error Clusters in Communication Systems and the Concept of Conditional Stationarity , 1965 .

[4]  F. Papangelou Integrability of expected increments of point processes and a related random change of scale , 1972 .

[5]  Yosihiko Ogata,et al.  Statistical Models for Earthquake Occurrences and Residual Analysis for Point Processes , 1988 .

[6]  Steven B. Lowen,et al.  ESTIMATION AND SIMULATION OF FRACTAL STOCHASTIC POINT PROCESSES , 1995 .

[7]  Eric P. Smith,et al.  An Introduction to Statistical Modeling of Extreme Values , 2002, Technometrics.

[8]  Jeffrey D. Scargle,et al.  Fractal-Based Point Processes , 2007, Technometrics.

[9]  F. De Rango,et al.  Markovian approach to model Underwater Acoustic channel: Techniques comparison , 2008, MILCOM 2008 - 2008 IEEE Military Communications Conference.

[10]  B. Arnold Pareto and Generalized Pareto Distributions , 2008 .

[11]  Michele Zorzi,et al.  World ocean simulation system (WOSS): a simulation tool for underwater networks with realistic propagation modeling , 2009, WUWNet.

[12]  Mehul Motani,et al.  Unified simulation and implementation software framework for underwater MAC protocol development , 2009, OCEANS 2009.

[13]  Christophe Pouzat,et al.  On Goodness of Fit Tests For Models of Neuronal Spike Trains Considered as Counting Processes , 2009, 0909.2785.

[14]  Daniel Slottje,et al.  Modeling income distributions and Lorenz curves , 2010 .

[15]  Mark A. Kramer,et al.  Drawing inferences from Fano factor calculations , 2010, Journal of Neuroscience Methods.

[16]  Michele Zorzi,et al.  On modeling JANUS packet errors over a shallow water acoustic channel using Markov and hidden Markov models , 2010, 2010 - MILCOM 2010 MILITARY COMMUNICATIONS CONFERENCE.

[17]  Konstantinos Pelekanakis,et al.  NATURAL GRADIENT-BASED ADAPTIVE ALGORITHMS FOR SPARSE UNDERWATER ACOUSTIC CHANNEL IDENTIFICATION , 2011 .

[18]  Lee Freitag,et al.  From underwater simulation to at-sea testing using the ns-2 network simulator , 2011, OCEANS 2011 IEEE - Spain.

[19]  M. Chitre,et al.  The UNET-2 modem — An extensible tool for underwater networking research , 2012, 2012 Oceans - Yeosu.

[20]  R. Masiero,et al.  DESERT Underwater: An NS-Miracle-based framework to design, simulate, emulate and realize test-beds for underwater network protocols , 2012, 2012 Oceans - Yeosu.

[21]  M. Chitre,et al.  New Sparse Adaptive Algorithms Based on the Natural Gradient and the ${L}_{0}$ -Norm , 2013, IEEE Journal of Oceanic Engineering.