Performance evaluation of Bayesian decision feedback equalizer with M-PAM symbols using importance sampling simulation
Performance evaluation of Bayesian decision feedback equalizer with M-PAM symbols using importance sampling simulation
An importance sampling (IS) simulation method is presented for evaluating the lower-bound symbol error rate (SER) of the Bayesian decision feedback equalizer (DFE) with $M$-PAM symbols, under the assumption of correct decision feedback. By exploiting an asymptotic property of the Bayesian DFE, a design procedure is developed, which chooses appropriate bias vectors for the simulation density to ensure asymptotic efficiency (AE) of the IS simulation.
4 pages
Chen, S.
9310a111-f79a-48b8-98c7-383ca93cbb80
December 2002
Chen, S.
9310a111-f79a-48b8-98c7-383ca93cbb80
Chen, S.
(2002)
Performance evaluation of Bayesian decision feedback equalizer with M-PAM symbols using importance sampling simulation.
2nd IMA Int. Conf. Mathematics in Communications, Lancaster, United Kingdom.
16 - 18 Dec 2002.
.
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Conference or Workshop Item
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Abstract
An importance sampling (IS) simulation method is presented for evaluating the lower-bound symbol error rate (SER) of the Bayesian decision feedback equalizer (DFE) with $M$-PAM symbols, under the assumption of correct decision feedback. By exploiting an asymptotic property of the Bayesian DFE, 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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Published date: December 2002
Additional Information:
presented at 2nd IMA Int. Cof. Mathematics in Communications (Lancaster, UK), Dec. 16-18 2002 Event Dates: December 16-18, 2002 Organisation: IMA
Venue - Dates:
2nd IMA Int. Conf. Mathematics in Communications, Lancaster, United Kingdom, 2002-12-16 - 2002-12-18
Organisations:
Southampton Wireless Group
Identifiers
Local EPrints ID: 257205
URI: http://eprints.soton.ac.uk/id/eprint/257205
PURE UUID: 1331efb3-0f97-4cd6-9893-60632c9eba98
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Date deposited: 31 Jan 2003
Last modified: 14 Mar 2024 05:54
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Author:
S. Chen
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