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Optimum lag and subset selection for radial basis function equaliser

Optimum lag and subset selection for radial basis function equaliser
Optimum lag and subset selection for radial basis function equaliser
This paper examines the application of the radial basis function (RBF) network to the modelling of the Bayesian equaliser. In particular, we study the effects of delay order d on decision boundary and
attainable bit error rate (BFR) performance. To determine the optimum delay parameter for minimum BER performance, a simple BER estimator is proposed. The implementation complexity of the RBF network grows exponentially with respect to the number of input nodes. As such, the full implementation of the RBF network to realise the Bayesian solution may not be feasible. To reduce some of the implementation complexity, we propose an algorithm to perform subset model selection. Our
results indicate that it is possible to reduce model size without, significant degradation in BER performance.
593-602
Chng, E. S.
fea46228-47bf-4963-83d7-b7b5591183de
Mulgrew, B.
95a3fbda-7de2-4583-b1f2-0a54a69b414a
Chen, S.
9310a111-f79a-48b8-98c7-383ca93cbb80
Gibson, G.
f4897d59-a729-4a88-94c5-9588cfe803ed
Chng, E. S.
fea46228-47bf-4963-83d7-b7b5591183de
Mulgrew, B.
95a3fbda-7de2-4583-b1f2-0a54a69b414a
Chen, S.
9310a111-f79a-48b8-98c7-383ca93cbb80
Gibson, G.
f4897d59-a729-4a88-94c5-9588cfe803ed

Chng, E. S., Mulgrew, B., Chen, S. and Gibson, G. (1995) Optimum lag and subset selection for radial basis function equaliser. 5th IEEE Workshop on Neural Networks for Signal Processing, Cambridge, United States. 30 Aug - 01 Sep 1995. pp. 593-602 .

Record type: Conference or Workshop Item (Paper)

Abstract

This paper examines the application of the radial basis function (RBF) network to the modelling of the Bayesian equaliser. In particular, we study the effects of delay order d on decision boundary and
attainable bit error rate (BFR) performance. To determine the optimum delay parameter for minimum BER performance, a simple BER estimator is proposed. The implementation complexity of the RBF network grows exponentially with respect to the number of input nodes. As such, the full implementation of the RBF network to realise the Bayesian solution may not be feasible. To reduce some of the implementation complexity, we propose an algorithm to perform subset model selection. Our
results indicate that it is possible to reduce model size without, significant degradation in BER performance.

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More information

Published date: 1995
Additional Information: 5th IEEE Workshop on Neural Networks for Signal Processing (Cambridge, USA), Aug. 31-Sept. 2, 1995. Event Dates: Aug. 31-Sept. 2, 1995 Organisation: IEEE Signal Processing Society
Venue - Dates: 5th IEEE Workshop on Neural Networks for Signal Processing, Cambridge, United States, 1995-08-30 - 1995-09-01
Organisations: Southampton Wireless Group

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Local EPrints ID: 251089
URI: http://eprints.soton.ac.uk/id/eprint/251089
PURE UUID: 5298baeb-26de-4dab-b966-3652d2d6bc74

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Date deposited: 12 Oct 1999
Last modified: 03 Feb 2022 17:48

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Contributors

Author: E. S. Chng
Author: B. Mulgrew
Author: S. Chen
Author: G. Gibson

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