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Importance Sampling Simulation and Multiple-Hyperplane Realization of the Bayesian Decision Feedback Equaliser

Importance Sampling Simulation and Multiple-Hyperplane Realization of the Bayesian Decision Feedback Equaliser
Importance Sampling Simulation and Multiple-Hyperplane Realization of the Bayesian Decision Feedback Equaliser
For the class of equalisers that employs a symbol-decision finite-memory structure with decision feedback, the optimal solution is known to be the Bayesian decision feedback equaliser (DFE). The complexity of the optimal Bayesian DFE however increases exponentially with the length of the channel impulse response (CIR). It has been noted that, when the signal to noise ratio (SNR) tends to infinity, the decision boundary of the Bayesian DFE is asymptotically piecewise linear and consists of several hyperplanes. This asymptotic property can be exploited for efficient simulation and implementation of the Bayesian DFE. An importance sampling (IS) simulation technique is presented based on this asymptotic property for evaluating the lower-bound bit error rate (BER) of the Bayesian DFE under the assumption of correct decisions being fed back. A design procedure is developed, which chooses appropriate bias vectors for the simulation density to ensure asymptotic efficiency of the IS simulation. As the set of hyperplanes that form the asymptotic Bayesian decision boundary can easily be found, they can be used to partition the observation space. The resulting multiple-hyperplane detector can closely approximate the optimal Bayesian detector, at an advantage of considerably reduced decision omplexity.
0-19-850734-8
157-167
Oxford University Press
Chen, S.
ac405529-3375-471a-8257-bda5c0d10e53
Hanzo, L.
66e7266f-3066-4fc0-8391-e000acce71a1
McWhirter, J.
Proudler, I.K.
Chen, S.
ac405529-3375-471a-8257-bda5c0d10e53
Hanzo, L.
66e7266f-3066-4fc0-8391-e000acce71a1
McWhirter, J.
Proudler, I.K.

Chen, S. and Hanzo, L. (2002) Importance Sampling Simulation and Multiple-Hyperplane Realization of the Bayesian Decision Feedback Equaliser. In, McWhirter, J. and Proudler, I.K. (eds.) Mathematics in Signal Processing V. (IMA Conference Series) Mathematics in Signal Processing V (31/03/02) Oxford University Press, pp. 157-167.

Record type: Book Section

Abstract

For the class of equalisers that employs a symbol-decision finite-memory structure with decision feedback, the optimal solution is known to be the Bayesian decision feedback equaliser (DFE). The complexity of the optimal Bayesian DFE however increases exponentially with the length of the channel impulse response (CIR). It has been noted that, when the signal to noise ratio (SNR) tends to infinity, the decision boundary of the Bayesian DFE is asymptotically piecewise linear and consists of several hyperplanes. This asymptotic property can be exploited for efficient simulation and implementation of the Bayesian DFE. An importance sampling (IS) simulation technique is presented based on this asymptotic property for evaluating the lower-bound bit error rate (BER) of the Bayesian DFE under the assumption of correct decisions being fed back. A design procedure is developed, which chooses appropriate bias vectors for the simulation density to ensure asymptotic efficiency of the IS simulation. As the set of hyperplanes that form the asymptotic Bayesian decision boundary can easily be found, they can be used to partition the observation space. The resulting multiple-hyperplane detector can closely approximate the optimal Bayesian detector, at an advantage of considerably reduced decision omplexity.

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Published date: April 2002
Additional Information: Chapter: 14
Venue - Dates: Mathematics in Signal Processing V, 2002-03-31
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 255964
URI: http://eprints.soton.ac.uk/id/eprint/255964
ISBN: 0-19-850734-8
PURE UUID: 29bc53f3-62b2-4a8d-baa8-03455ba99727
ORCID for L. Hanzo: ORCID iD orcid.org/0000-0002-2636-5214

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Date deposited: 02 Dec 2003
Last modified: 23 Sep 2020 01:31

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