Adaptive equalization using the backpropagation algorithm

Equalization of satellite communication using complex-bilinear recurrent neural network (C-BLRNN) is proposed. Since the BLRNN is based on the bilinear polynomial, it can be used in modeling highly nonlinear systems with time-series characteristics more effectively than multilayer perceptron type neural networks (MLPNN). The BLRNN is first expanded to its complex value version (C-BLRNN) for dealing with the complex input values in the paper. C-BLRNN is then applied to equalization of a digital satellite communication channel for M-PSK and QAM, which has severe nonlinearity with memory due to traveling wave tube amplifier (TWTA). The proposed C-BLRNN equalizer for a channel model is compared with the currently used Volterra filter equalizer or decision feedback equalizer (DFE), and conventional complex-MLPNN equalizer. The results show that the proposed C-BLRNN equalizer gives very favorable results in both the MSE and BER criteria over Volterra filter equalizer, DFE, and complex-MLPNN equalizer.

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“…Consider (12) where Hence (13) Similarly, the following is derived (14) By substituting (13) and (14) into (12), we have R…”

Section: R Rmentioning

confidence: 99%

Complex-bilinear recurrent neural network for equalization of a digital satellite channel

Park

Jeong

2002

IEEE Trans. Neural Netw.

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“…Nonlinear communication channel equalization using real or complex-valued neural networks (NN's) has been deeply studied in the last few years, and several authors have recognized the usefulness of recurrent [11] or multilayer perceptron (MLP) NN's architectures [12]- [15]. One of the main problems involved in using these techniques is the very long data sequence required in the learning phase that, in turn, leads to an increase in the adaptation errors when tracking a time-varying channel.…”

Section: Introductionmentioning

confidence: 99%

Complex-valued neural networks with adaptive spline activation function for digital-radio-links nonlinear equalization

Uncini

Vecci

Campolucci

et al. 1999

IEEE Trans. Signal Process.

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In this paper, a new complex-valued neural network based on adaptive activation functions is proposed. By varying the control points of a pair of Catmull-Rom cubic splines, which are used as an adaptable activation function, this new kind of neural network can be implemented as a very simple structure that is able to improve the generalization capabilities using few training samples. Due to its low architectural complexity (low overhead with respect to a simple FIR filter), this network can be used to cope with several nonlinear DSP problems at a high symbol rate. In particular, this work addresses the problem of nonlinear channel equalization. In fact, although several authors have already recognized the usefulness of a neural network as a channel equalizer, one problem has not yet been addressed: the high complexity and the very long data sequence needed to train the network. Several experimental results using a realistic channel model are reported that prove the effectiveness of the proposed network on equalizing a digital satellite radio link in the presence of noise, nonlinearities, and intersymbol interference (ISI).

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“…2(b)] was an NN used as an FIR filter [37], [38]. A fully connected, feed-forward, threelayer NN was used.…”

Section: B Discriminator Detailsmentioning

confidence: 99%

Detection, classification, and superposition resolution of action potentials in multiunit single-channel recordings by an on-line real-time neural network

Chandra

Optican

1997

IEEE Trans. Biomed. Eng.

105

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Abstract-Determination of single-unit spike trains from multiunit recordings obtained during extracellular recording has been the focus of many studies over the last two decades. In multiunit recordings, superpositions can occur with high frequency if the firing rates of the neurons are high or correlated, making superposition resolution imperative for accurate spike train determination. In this work, a connectionist neural network (NN) was applied to the spike sorting challenge. A novel training scheme was developed which enabled the NN to resolve some superpositions using single-channel recordings. Simulated multiunit spike trains were constructed from templates and noise segments that were extracted from real extracellular recordings. The simulations were used to determine the performances of the NN and a simple matched template filter (MTF), which was used as a basis for comparison. The network performed as well as the MTF in identifying nonoverlapping spikes, and was significantly better in resolving superpositions and rejecting noise. An on-line, real-time implementation of the NN discriminator, using a highspeed digital signal processor mounted inside an IBM-PC, is now in use in six laboratories.

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Adaptive equalization using the backpropagation algorithm

Cited by 15 publications

References 2 publications

Complex-bilinear recurrent neural network for equalization of a digital satellite channel

Complex-bilinear recurrent neural network for equalization of a digital satellite channel

Complex-valued neural networks with adaptive spline activation function for digital-radio-links nonlinear equalization

Detection, classification, and superposition resolution of action potentials in multiunit single-channel recordings by an on-line real-time neural network

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