Paper Info
Title
Multiple channel crosstalk removal using limited connectivity neural networks
Abstract
Limited connectivity neural network architectures are investigated for the removal of crosstalk in systems using mutually overlapping sub-channels for the communication of multiple signals, either analogue or digital. The crosstalk error is modelled such that a fixed proportion of the signals in adjacent channels is added to the main signal. Different types of neural networks, trained using gradient descent algorithms, are tested as to their suitability for reducing the errors caused by a combination of crosstalk and additional gaussian noise. In particular we propose a single layer limited connectivity neural network since it promises to be the most easily implemented in hardware. A variable gain neuron structure is described which can be used for both analogue and digital data.
Year
DOI
Venue
1996
10.1109/ICECS.1996.584614
Electronics, Circuits, and Systems, 1996. ICECS '96., Proceedings of the Third IEEE International Conference
Keywords
DocType
Volume
Gaussian noise,adjacent channel interference,cochannel interference,crosstalk,feedforward neural nets,frequency division multiplexing,interference suppression,learning (artificial intelligence),telecommunication computing,FDM,additional gaussian noise,adjacent channels,analogue signals,crosstalk error modelling,digital signals,gradient descent algorithms,limited connectivity neural networks,multiple channel crosstalk removal,multiple signal communication,mutually overlapping sub-channels,neural network training,variable gain neuron structure
Conference
2
ISBN
Citations 
PageRank 
0-7803-3650-X
0
0.34
References 
Authors
1
3
Name
Order
Citations
PageRank
Michael P. Craven140.86
Curtis, K.M.200.34
Hayes-Gill, B.R.300.34