Error Analysis for ML Sequence Detection in ISI Channels With Gauss Markov Noise

We consider error probability analysis for a class of ISI channels with data-dependent Gauss-Markov noise and propose an efficiently computable BER upper bound that is tight in the high SNR regime.

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Bibliographische Detailangaben
Veröffentlicht in:	IEEE transactions on signal processing 2013-07, Vol.61 (14), p.3647-3652
Hauptverfasser:	Kumar, Naveen, Ramamoorthy, Aditya, Salapaka, Murti V.
Format:	Artikel
Sprache:	eng
Schlagworte:	Applied sciences Channel models Channels Decoding Detection, estimation, filtering, equalization, prediction Detectors Error analysis error-state diagram Exact sciences and technology flowgraph techniques Information, signal and communications theory Inter-symbol-interference channels Markov processes Noise performance analysis Signal and communications theory Signal processing Signal to noise ratio Signal, noise Simulation Telecommunications and information theory Transaction processing Upper bound Upper bounds Viterbi algorithm
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Beschreibung
Zusammenfassung:	We consider error probability analysis for a class of ISI channels with data-dependent Gauss-Markov noise and propose an efficiently computable BER upper bound that is tight in the high SNR regime.
ISSN:	1053-587X 1941-0476
DOI:	10.1109/TSP.2013.2262274