Performance of Generalized Regression Neural Network-based channel estimation in Vectored DSL systems

It is well-known that Vectored Digital Subscriber Line (DSL) transmission promises significant theoretical data-rate increases for DSL technology; however, Vectored DSL requires full knowledge of the channel. The effectiveness of Vectored DSL transmission in a practical setting, where channel knowle...

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Bibliographic Details
Published in2012 25th IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) pp. 1 - 5
Main Authors Huberman, S., Tho Le-Ngoc
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2012
Subjects
Channel Estimation
Computers
Conferences
Digital Subscriber Line (DSL)
Generalized Regression Neural Network
Vectored DSL
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Summary:It is well-known that Vectored Digital Subscriber Line (DSL) transmission promises significant theoretical data-rate increases for DSL technology; however, Vectored DSL requires full knowledge of the channel. The effectiveness of Vectored DSL transmission in a practical setting, where channel knowledge is subject to error, has yet to be determined. This paper proposes a Generalized Regression Neural Network (GRNN)-based approach to DSL channel estimation by interpolating between a subset of measured or estimated data-points. Furthermore, closed-form expressions for the effect of channel estimation error on he achievable Vectored DSL data-rate are derived, using a Zero-Forcing (ZF) interference canceller for upstream transmission and a Diagonalizing Pre-coder (DP) for downstream transmission. Finally, simulation results are provided to demonstrate the performance loss associated with channel estimation error for Vectored DSL transmission, based on the ANN approach and a linear regression approach.
ISBN:1467314315
9781467314312
ISSN:0840-7789
DOI:10.1109/CCECE.2012.6334880