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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| Vydané v: | 2012 25th IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) s. 1 - 5 |
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| Hlavní autori: | , |
| Médium: | Konferenčný príspevok.. |
| Jazyk: | English |
| Vydavateľské údaje: |
IEEE
01.04.2012
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| Predmet: | |
| ISBN: | 1467314315, 9781467314312 |
| ISSN: | 0840-7789 |
| On-line prístup: | Získať plný text |
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| Shrnutí: | 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. |
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| ISBN: | 1467314315 9781467314312 |
| ISSN: | 0840-7789 |
| DOI: | 10.1109/CCECE.2012.6334880 |