A data-driven framework for rapid modeling of wireless communication channels
Loading...
Authors
Horner, Douglas
Subjects
Wireless Connectivity Maps
Random Fields
Kriging
Gaussian Process Models
`1 Regularized Logistic Regression
Kalman Filtering
Underwater Acoustic Networking
Random Fields
Kriging
Gaussian Process Models
`1 Regularized Logistic Regression
Kalman Filtering
Underwater Acoustic Networking
Advisors
Xie, Geoffrey
Date of Issue
2013-12
Date
Dec-13
Publisher
Monterey, California: Naval Postgraduate School
Language
Abstract
Accurate estimation and prediction of wireless signal strength holds the promise to improve a wide variety of applications in network-ing and unmanned systems. Current estimation approaches use either simplistic attenuation equations or detailed physical models that provide limited accuracy and may require a lengthy period of environmental assessment and computation. This dissertation presents a new, data-driven, stochastic framework for rapidly building accurate wireless connectivity maps. The framework advances the state of the art in three aspects. First, it augments the classic spatial interpolation procedure known as Kriging with a complementary additive approach to capture the typical anisotropic nature of wireless channels in cluttered environments. Second, it includes a technique for rapidly creating and maintaining a connectivity map in near real-time through the use of a spatial Bayesian recursive filter. Third, it introduces a novel methodology to adapt the resolution of a connectivity map based on the spatial characteristics and the quantity of available sample measurements. Detailed analyses, using several datasets collected recently in the Monterey Harbor, have confirmed the power and agility of the proposed approach.
Type
Thesis
Description
Series/Report No
Department
Computer Science
Organization
Identifiers
NPS Report Number
Sponsors
Funder
Format
Citation
Distribution Statement
Approved for public release; distribution is unlimited.
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.