Gaussian Process Techniques for Wireless Communications
Abstract
Bayesian filtering is a general framework for recursively estimating the state of a dynamical system. Classical solutions such that Kalman filter and Particle filter are introduced in this report. Gaussian processes have been introduced as a non-parametric technique for system estimation from supervision learning. For the thesis project, we intend to propose a new, general methodology for inference and learning in non-linear state-space models probabilistically incorporating with the Gaussian process model estimation.
Cite
@article{arxiv.1011.0786,
title = {Gaussian Process Techniques for Wireless Communications},
author = {Mr. Chong Han and Dr. Ido Nevat and Dr. Gareth Peters and Prof. Jinhong Yuan},
journal= {arXiv preprint arXiv:1011.0786},
year = {2010}
}
Comments
This is a Thesis A report submitted to School of Electrical Engineering & Telecommunications, University of New South Wales, Australia, based on the work done in Semester 2, 2010. Research work is to be continued in Semester 1, 2011