tailieunhanh - Báo cáo hóa học: "Research Article Digital Communication Receivers Using Gaussian Processes for Machine Learning"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Digital Communication Receivers Using Gaussian Processes for Machine Learning | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2008 Article ID 491503 12 pages doi 2008 491503 Research Article Digital Communication Receivers Using Gaussian Processes for Machine Learning Fernando Perez-Cruz1 2 and Juan Josts Murillo-Fuentes3 1 Department of Electrical Engineering Princeton University Princeton NJ 08544 USA 2 Department of Signal Theory and Communications Carlos III University of Madrid Avda. Universidad 30 28911 Leganes Spain 3Departamento de Teoria de la Senaly Comunicaciones Escuela Tecnica Superior de Ingenieros Universidad de Sevilla Paseo de los Descubrimientos s n 41092 Sevilla Spain Correspondence should be addressed to Fernando Perez-Cruz fp@ Received 13 October 2007 Revised 18 March 2008 Accepted 19 May 2008 Recommended by Anibal Figueiras-Vidal 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 .