Title :
Joint Nonlinear Channel Equalization and Soft LDPC Decoding With Gaussian Processes
Author :
Olmos, Pablo M. ; Murillo-Fuentes, Juan José ; Pérez-Cruz, Fernando
Author_Institution :
Dept. Teor. de la Senal y Comun., Univ. de Sevilla, Sevilla, Spain
fDate :
3/1/2010 12:00:00 AM
Abstract :
In this paper, we introduce a new approach for nonlinear equalization based on Gaussian processes for classification (GPC). We propose to measure the performance of this equalizer after a low-density parity-check channel decoder has detected the received sequence. Typically, most channel equalizers concentrate on reducing the bit error rate, instead of providing accurate posterior probability estimates. We show that the accuracy of these estimates is essential for optimal performance of the channel decoder and that the error rate output by the equalizer might be irrelevant to understand the performance of the overall communication receiver. In this sense, GPC is a Bayesian nonlinear classification tool that provides accurate posterior probability estimates with short training sequences. In the experimental section, we compare the proposed GPC-based equalizer with state-of-the-art solutions to illustrate its improved performance.
Keywords :
Gaussian processes; channel coding; channel estimation; equalisers; error statistics; nonlinear codes; parity check codes; Bayesian nonlinear classification tool; GPC; Gaussian processes; bit error rate; channel equalizers; joint nonlinear channel equalization; low-density parity-check channel decoder; posterior probability estimates; soft LDPC decoding; Coding; Gaussian processes; equalization; low-density parity-check (LDPC); machine learning; nonlinear channel; soft-decoding; support vector machine (SVM);
Journal_Title :
Signal Processing, IEEE Transactions on
DOI :
10.1109/TSP.2009.2034941