Iterative Receiver Design for ISI Channels Using Combined Belief- and Expectation-Propagation

Peng Sun, Chuanzong Zhang, Zhongyong Wang, Carles Navarro Manchón, Bernard Henri Fleury

Research output: Contribution to journalLetterResearchpeer-review

17 Citations (Scopus)
465 Downloads (Pure)

Abstract

In this letter, a message-passing algorithm that combines belief propagation and expectation propagation is applied to design an iterative receiver for intersymbol interference channels. We detail the derivation of the messages passed along the nodes of a vector-form factor graph representing the underlying probabilistic model. We also present a simple but efficient method to cope with the "negative variance" problem of expectation propagation. Simulation results show that the proposed algorithm outperforms, in terms of bit-error-rate and convergence rate, a LMMSE turbo-equalizer based on Gaussian message passing with the same order of computational complexity.
Original languageEnglish
JournalI E E E Signal Processing Letters
Volume22
Issue number10
Pages (from-to)1733 - 1737
ISSN1070-9908
DOIs
Publication statusPublished - 19 Feb 2015

Fingerprint

Message passing
Receiver
Propagation
Message-passing Algorithms
Factor Graph
Intersymbol Interference
Intersymbol interference
Interference Channel
Belief Propagation
Equalizer
Form Factors
Equalizers
Message Passing
Probabilistic Model
Bit error rate
Error Rate
Convergence Rate
Computational complexity
Computational Complexity
Vertex of a graph

Keywords

  • Belief Propagation
  • Expectation Propagation
  • Turbo Equalization

Cite this

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title = "Iterative Receiver Design for ISI Channels Using Combined Belief- and Expectation-Propagation",
abstract = "In this letter, a message-passing algorithm that combines belief propagation and expectation propagation is applied to design an iterative receiver for intersymbol interference channels. We detail the derivation of the messages passed along the nodes of a vector-form factor graph representing the underlying probabilistic model. We also present a simple but efficient method to cope with the {"}negative variance{"} problem of expectation propagation. Simulation results show that the proposed algorithm outperforms, in terms of bit-error-rate and convergence rate, a LMMSE turbo-equalizer based on Gaussian message passing with the same order of computational complexity.",
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year = "2015",
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Iterative Receiver Design for ISI Channels Using Combined Belief- and Expectation-Propagation. / Sun, Peng; Zhang, Chuanzong; Wang, Zhongyong; Manchón, Carles Navarro; Fleury, Bernard Henri.

In: I E E E Signal Processing Letters, Vol. 22, No. 10, 19.02.2015, p. 1733 - 1737.

Research output: Contribution to journalLetterResearchpeer-review

TY - JOUR

T1 - Iterative Receiver Design for ISI Channels Using Combined Belief- and Expectation-Propagation

AU - Sun, Peng

AU - Zhang, Chuanzong

AU - Wang, Zhongyong

AU - Manchón, Carles Navarro

AU - Fleury, Bernard Henri

PY - 2015/2/19

Y1 - 2015/2/19

N2 - In this letter, a message-passing algorithm that combines belief propagation and expectation propagation is applied to design an iterative receiver for intersymbol interference channels. We detail the derivation of the messages passed along the nodes of a vector-form factor graph representing the underlying probabilistic model. We also present a simple but efficient method to cope with the "negative variance" problem of expectation propagation. Simulation results show that the proposed algorithm outperforms, in terms of bit-error-rate and convergence rate, a LMMSE turbo-equalizer based on Gaussian message passing with the same order of computational complexity.

AB - In this letter, a message-passing algorithm that combines belief propagation and expectation propagation is applied to design an iterative receiver for intersymbol interference channels. We detail the derivation of the messages passed along the nodes of a vector-form factor graph representing the underlying probabilistic model. We also present a simple but efficient method to cope with the "negative variance" problem of expectation propagation. Simulation results show that the proposed algorithm outperforms, in terms of bit-error-rate and convergence rate, a LMMSE turbo-equalizer based on Gaussian message passing with the same order of computational complexity.

KW - Belief Propagation

KW - Expectation Propagation

KW - Turbo Equalization

U2 - 10.1109/LSP.2015.2404822

DO - 10.1109/LSP.2015.2404822

M3 - Letter

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JF - I E E E Signal Processing Letters

SN - 1070-9908

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