A Systematic Framework for Iterative Maximum Likelihood Receiver Design

In this paper, we link the turbo principle to unconstrained maximum likelihood (ML) sequence detection and joint ML parameter estimation. First, we demonstrate for memoryless channels with complete channel state information how the turbo decoder can be systematically derived starting from the ML seq...

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Vydáno v:IEEE transactions on communications Ročník 58; číslo 7; s. 2035 - 2045
Hlavní autoři: Schmitt, Lars, Meyr, Heinrich
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.07.2010
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Approximation
Channel state information
Channels
Concatenated codes
Decoding
Design engineering
Equations
Equivalence
Fading
Iterative algorithms
Iterative decoding
iterative receiver design
Mathematical analysis
Maximum likelihood decoding
Maximum likelihood detection
Maximum likelihood estimation
Memoryless systems
Parameter estimation
Receivers
soft-symbol estimation
turbo decoding
turbo estimation
ISSN:0090-6778, 1558-0857
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Shrnutí:In this paper, we link the turbo principle to unconstrained maximum likelihood (ML) sequence detection and joint ML parameter estimation. First, we demonstrate for memoryless channels with complete channel state information how the turbo decoder can be systematically derived starting from the ML sequence detection criterion. In particular, we show that a method to solve the ML sequence detection problem is to iteratively solve the corresponding critical point equations of an equivalent unconstrained estimation problem by means of fixed-point iterations. The turbo decoding algorithm is obtained by approximating the overall a posteriori probabilities. Subsequently, we show how this general approximative iterative maximum likelihood (AIML) framework can be applied to general iterative ML receiver design. We consider static memoryless channels with unknown channel parameters. The time-selective fading channels with partial channel state information is the subject of a companion paper.
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ISSN:0090-6778
1558-0857
DOI:10.1109/TCOMM.2010.07.080357