Open Access

Digital Communication Receivers Using Gaussian Processes for Machine Learning

EURASIP Journal on Advances in Signal Processing20082008:491503

Received: 13 October 2007

Accepted: 19 May 2008

Published: 2 June 2008


We propose Gaussian processes (GPs) as a novel nonlinear receiver for digital communication systems. The GPs framework can be used to solve both classification (GPC) and regression (GPR) problems. The minimum mean squared error solution is the expectation of the transmitted symbol given the information at the receiver, which is a nonlinear function of the received symbols for discrete inputs. GPR can be presented as a nonlinear MMSE estimator and thus capable of achieving optimal performance from MMSE viewpoint. Also, the design of digital communication receivers can be viewed as a detection problem, for which GPC is specially suited as it assigns posterior probabilities to each transmitted symbol. We explore the suitability of GPs as nonlinear digital communication receivers. GPs are Bayesian machine learning tools that formulates a likelihood function for its hyperparameters, which can then be set optimally. GPs outperform state-of-the-art nonlinear machine learning approaches that prespecify their hyperparameters or rely on cross validation. We illustrate the advantages of GPs as digital communication receivers for linear and nonlinear channel models for short training sequences and compare them to state-of-the-art nonlinear machine learning tools, such as support vector machines.

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Authors’ Affiliations

Department of Electrical Engineering, Princeton University
Department of Signal Theory and Communications, Carlos III University of Madrid
Departamento de Teoría de la Señal y Comunicaciones, Escuela Técnica Superior de Ingenieros, Universidad de Sevilla


© F. Pérez-Cruz and J. J. Murillo-Fuentes. 2008

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.