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Importance sampling simulation for evaluating lower-bound symbol error rate of the Bayesian DFE with multi-level signalling schemes

Importance sampling simulation for evaluating lower-bound symbol error rate of the Bayesian DFE with multi-level signalling schemes
Importance sampling simulation for evaluating lower-bound symbol error rate of the Bayesian DFE with multi-level signalling schemes
For the class of equalizers that employs a symbol-decision finite-memory structure with decision feedback, the optimal solution is known to be the Bayesian decision feedback equalizer (DFE). The complexity of the Bayesian DFE however increases exponentially with the length of the channel impulse response (CIR) and the size of the symbol constellation. Conventional Monte Carlo simulation for evaluation the symbol error rate (SER) of the Bayesian DFE becomes impossible for high channel signal to noise ratio (SNR) conditions. It has been noted that the optimal Bayesian decision boundary separating any two neighbouring signal classes is asymptotically piecewise linear and consists of several hyperplanes, when the SNR tends to infinity. This asymptotic property can be exploited for efficient simulation of the Bayesian DFE. An importance sampling (IS) simulation technique is presented based on this asymptotic property for evaluating the lower-bound SER of the Bayesian DFE with multi-level pulse amplitude modulation ($M$-PAM) schemes, 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 (AE) of the IS simulation.
1053-587X
1229-1236
Chen, S.
ac405529-3375-471a-8257-bda5c0d10e53
Chen, S.
ac405529-3375-471a-8257-bda5c0d10e53

Chen, S. (2002) Importance sampling simulation for evaluating lower-bound symbol error rate of the Bayesian DFE with multi-level signalling schemes. IEEE Transactions on Signal Processing, 50 (5), 1229-1236.

Record type: Article

Abstract

For the class of equalizers that employs a symbol-decision finite-memory structure with decision feedback, the optimal solution is known to be the Bayesian decision feedback equalizer (DFE). The complexity of the Bayesian DFE however increases exponentially with the length of the channel impulse response (CIR) and the size of the symbol constellation. Conventional Monte Carlo simulation for evaluation the symbol error rate (SER) of the Bayesian DFE becomes impossible for high channel signal to noise ratio (SNR) conditions. It has been noted that the optimal Bayesian decision boundary separating any two neighbouring signal classes is asymptotically piecewise linear and consists of several hyperplanes, when the SNR tends to infinity. This asymptotic property can be exploited for efficient simulation of the Bayesian DFE. An importance sampling (IS) simulation technique is presented based on this asymptotic property for evaluating the lower-bound SER of the Bayesian DFE with multi-level pulse amplitude modulation ($M$-PAM) schemes, 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 (AE) of the IS simulation.

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More information

Published date: May 2002
Additional Information: submitted for publication in May 2001, revised in Oct. 2001, accepted in Jan. 2002
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 255735
URI: http://eprints.soton.ac.uk/id/eprint/255735
ISSN: 1053-587X
PURE UUID: 9a8eb42a-6b10-4ee8-ae9c-c17f18af4ce6

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Date deposited: 04 Mar 2004
Last modified: 23 Apr 2020 16:54

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