Application of Multilayer Perceptrons as Adaptive Channel Equalisers

Gibson, Gavin J.; Siu, S.; Cowan, C.F.N.

doi:10.1016/s1474-6670(17)52779-2

Cited by 8 publications

(16 citation statements)

References 3 publications

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“…a was set to 1.0 when noise level was high and was gradually increased to 8.0 as the signal-to-noise ratio improved. A similar simulation study was given by Gibson et al [4] for the multi-layer perceptron equaliser and the results were very close to the present simulation study. The training of a polynomial-perceptron equaliser is, however, much easier compared with that of a multilayer perceptron equaliser.…”

Section: Decision Region Formed By Polynomial-perceptron Equalisersupporting

confidence: 92%

“…Using the Bayes decision rule, it is shown that the optimal equalisation solution is highly nonlinear, a result identical to that derived in [4] by a different approach. An old technique, namely polynomial approximation, is then employed as a means of approximately realising the optimal solution.…”

Section: Introductionmentioning

confidence: 52%

“…Nonlinear structures are therefore required to achieve fully or near optimal performance. Gibson et al [4] proposed a nonlinear equaliser structure based on the multilayer perceptron and demonstrated its superior performance over the linear equaliser. The multilayer perceptron has a very general ability of nonlinear decision making and, theoretically, a multilayer perceptron equaliser with sufficient size can realise the optimal performance.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive channel equalisation using a polynomial-perceptron structure

Chen

Gibson

Cowan

1990

IEE Proc. I Commun. Speech Vis.

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Abstract:The paper investigates the application of a simple nonlinear structure to the problem of adaptive channel equalisation. Based on the Bayes decision rule, it is shown that the optimal equalisation solution is an inherently nonlinear problem and, therefore, it is desired to incorporate some degree of nonlinearity in the design of equaliser structure. The approximate realisation of the optimal equalisation solution is implemented using a polynomial-perceptron architecture and simulation results are included to support the theoretical analysis. IntroductionCommunications channel equalisation is concerned with the reconstruction of digital signals that have been passed through a dispersive channel and then corrupted with additive noise. Traditional techniques for solving this equalisation problem are based on linear finite filters. Adaptive linear equalisers are robust and can easily be implemented. The operation of an equaliser at each sample instant is typically based on a finite number of channel observations and decisions are usually made on a symbol-by-symbol base. Even under this classical information constraint, it has been shown that channel equalisation is an inherently nonlinear problem [4] regardless of whether a channel is minimum or nonminimum phase. Nonlinear structures are therefore required to achieve fully or near optimal performance. Gibson et al.[4] proposed a nonlinear equaliser structure based on the multilayer perceptron and demonstrated its superior performance over the linear equaliser. The multilayer perceptron has a very general ability of nonlinear decision making and, theoretically, a multilayer perceptron equaliser with sufficient size can realise the optimal performance. There are, however, some practical difficulties associated with this highly nonlinear structure that require further investigation. The selection of architecture and parameter values for the multilayer perceptron equaliser is mainly by experiment. The training [7], and training times are typically very long. Although the use of recursive Gauss-Newton algorithms [2, 91 can significantly improve the convergence properties of the multilayer perceptron equaliser, these algorithms require more computation at each recursion and will have difficulties in meeting the real-time requirements of high-speed data transmission where adaptive equalisation is mostly needed.In this paper an alternative nonlinear equaliser structure is examined. Using the Bayes decision rule, it is shown that the optimal equalisation solution is highly nonlinear, a result identical to that derived in [4] by a different approach. An old technique, namely polynomial approximation, is then employed as a means of approximately realising the optimal solution. This leads to a polynomial-perceptron equaliser that is theoretically more tractable compared with the multilayer perceptron equaliser as the filter parameters are almost linear with respect to the output error. Simple simulation examples are included to compare the performance of this polynomia...

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Section: Decision Region Formed By Polynomial-perceptron Equalisersupporting

confidence: 92%

Section: Introductionmentioning

confidence: 52%

Section: Introductionmentioning

confidence: 99%

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Adaptive channel equalisation using a polynomial-perceptron structure

Chen

Gibson

Cowan

1990

IEE Proc. I Commun. Speech Vis.

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“…This is however not true due to the noise enhancement. Previous research [5], [6] has demonstrated that the LTE does not achieve the full performance potential of the given symbol-decision structure in Fig. 2.…”

Section: Introduction Igh Speed Communications Channels Are Often mentioning

confidence: 95%

“…Better performance can be obtained if some more complex filtering method is employed. The use of the multilayer perception [5] and the polynomial filter [6] as equalizers can achieve significant performance improvement over the LTE. This is because these two nonlinear equalizers are able to approximate the optimal symbol-decision equalizer solution implicitly.…”

Section: Introduction Igh Speed Communications Channels Are Often mentioning

confidence: 99%

A clustering technique for digital communications channel equalization using radial basis function networks

Chen

Mulgrew

Grant

1993

IEEE Trans. Neural Netw.

674

303

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Abstruct-The paper investigates the application of a radial basis function network to digital communications channel equalization. It is shown that the radial basis function network has an identical structure to the optimal Bayesian symbol-decision equalizer solution and, therefore, can be employed to implement the Bayesian equalizer. The training of a radial basis function network to realize the Bayesian equalization solution can be achieved efficiently using a simple and robust supervised clustering algorithm. During data transmission a decision-directed version of the clustering algorithm enables the radial basis function network to track a slowly time-varying environment. Moreover, the clustering scheme provides an automatic compensation for nonlinear channel and equipment distortion. This represents a radically new approach to the adaptive equalizer design. Computer simulations are included to illustrate the analytical results.

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A practical radial basis function equalizer

Lee

Beach

Tepedelenlioglu

1999

IEEE Trans. Neural Netw.

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A radial basis function (RBF) equalizer design process has been developed in which the number of basis function centers used is substantially fewer than conventionally required. The reduction of centers is accomplished in two-steps. First an algorithm is used to select a reduced set of centers that lie close to the decision boundary. Then the centers in this reduced set are grouped, and an average position is chosen to represent each group. Channel order and delay, which are determining factors in setting the initial number of centers, are estimated from regression analysis. In simulation studies, an RBF equalizer with more than 2000-to-1 reduction in centers performed as well as the RBF equalizer without reduction in centers, and better than a conventional linear equalizer.

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Application of Multilayer Perceptrons as Adaptive Channel Equalisers

Cited by 8 publications

References 3 publications

Adaptive channel equalisation using a polynomial-perceptron structure

Adaptive channel equalisation using a polynomial-perceptron structure

A clustering technique for digital communications channel equalization using radial basis function networks

A practical radial basis function equalizer

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