2003
DOI: 10.1109/tcsi.2003.813966
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A neural-network-based channel-equalization strategy for chaos-based communication systems

Abstract: Abstract-This brief addresses the channel-distortion problem and proposes a technique for channel equalization in chaos-based communication systems. The proposed equalization is realized by a modified recurrent neural network incorporating a specific training (equalizing) algorithm.Index Terms-Channel equalization, chaos-based communications, recurrent neural networks (RNNs).

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Cited by 36 publications
(2 citation statements)
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“…Literature clearly demonstrates that the ANN is the most effective method for equalizing ISI induced by a communications channel [19][20][21][22][23]. The most popular ANN for channel equalization is the single layer multilayer perceptron (MLP).…”
Section: Artificial Neural Network Equalizermentioning
confidence: 99%
“…Literature clearly demonstrates that the ANN is the most effective method for equalizing ISI induced by a communications channel [19][20][21][22][23]. The most popular ANN for channel equalization is the single layer multilayer perceptron (MLP).…”
Section: Artificial Neural Network Equalizermentioning
confidence: 99%
“…However, blue filtering causes a reduction in the signal power, which results in a much lower SNR (Signal-to-Noise-Ratio) [5] . Alternatively, pre-and post-equalization schemes both in analogue [6,7] and digital [8,9] domains have been used to increase data rates R d . In recent years, utilization of spectrally efficient modulation formats such as OFDM (Orthogonal Frequency Division Multiplexing) has been the focus of increasing attention of researchers, to combat the bandwidth limitations of VLC systems [10] .…”
Section: Introductionmentioning
confidence: 99%