Using recurrent neural networks for adaptive communication channel equalization

dc.contributor.author	Κεχριώτης, Γιώργος Ι.	el
dc.contributor.author	Ζέρβας, Ευάγγελος	el
dc.contributor.author	Μανωλάκος, Ηλίας Σ.	el
dc.date.accessioned	2015-05-25T11:45:04Z
dc.date.issued	2015-05-25
dc.identifier.uri	http://hdl.handle.net/11400/11117
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/us/	*
dc.source	http://ieeexplore.ieee.org/	en
dc.subject	Data communication systems
dc.subject	Signal modulations
dc.subject	Neural networks
dc.subject	Συστήματα επικοινωνίας δεδομένων
dc.subject	Διαμορφώσεις σήματος
dc.subject	Νευρωνικά δίκτυα
dc.title	Using recurrent neural networks for adaptive communication channel equalization	en
heal.type	journalArticle
heal.classification	Technology
heal.classification	Electrical engineering
heal.classification	Τεχνολογία
heal.classification	Ηλεκτρολογία Μηχανολογία
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh85133147
heal.classificationURI	http://zbw.eu/stw/descriptor/18426-4
heal.classificationURI	N/A-Τεχνολογία
heal.classificationURI	N/A-Ηλεκτρολογία Μηχανολογία
heal.identifier.secondary	DOI: 10.1109/72.279190
heal.dateAvailable	10000-01-01
heal.language	en
heal.access	forever
heal.publicationDate	1994-03
heal.bibliographicCitation	KECHRIOTIS, G.I., ZERVAS, E. & MANOLAKOS, E.S. (1994). Using recurrent neural networks for adaptive communication channel equalization. IEEE Transactions on Neural Networks. [online] 5 (2). p. 267-278. Available from: http://ieeexplore.ieee.org/	en
heal.abstract	Recently, nonlinear adaptive filters based on a variety of neural network models have been used successfully for system identification and noise-cancellation in a wide class of applications. An important problem in data communications is that of channel equalization, i.e., the removal of interferences introduced by linear or nonlinear message corrupting mechanisms, so that the originally transmitted symbols can be recovered correctly at the receiver. In this paper we introduce an adaptive Recurrent Neural Network (RNN) based equalizer whose small size and high performance makes it suitable for high-speed channel equalization. We propose RNN based structures for both trained adaptation and blind equalization, and we evaluate their performance via extensive simulations for a variety of signal modulations and communication channel models. It is shown that the RNN equalizers have comparable performance with traditional linear filter based equalizers when the channel interferences are relatively mild, and that they outperform them by several orders of magnitude when either the channel's transfer function has spectral nulls or severe nonlinear distortion is present. In addition, the small-size RNN equalizers, being essentially generalized IIR filters, are shown to outperform multilayer perceptron equalizers of larger computational complexity in linear and non-linear channel equalization cases.	en
heal.publisher	IEEE	en
heal.journalName	IEEE Transactions on Neural Networks	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	false