Please use this identifier to cite or link to this item: https://ir.swu.ac.th/jspui/handle/123456789/14642
Title: Code-aided maximum-likelihood ambiguity resolution through free-energy minimization
Authors: Herzet C.
Woradit K.
Wymeersch H.
Vandendorpe L.
Keywords: Ambiguity resolution
Ambiguity resolution algorithms
Belief propagation
Data detection
Digital communications
Error correcting code
Graph representation
Low signal-to-noise ratio
Optimal receiver
Reduced complexity
Sequential implementation
Stopping criteria
Training sequences
Approximation algorithms
Digital communication systems
Information theory
Optimization
Radio systems
Security of data
Signal to noise ratio
Maximum likelihood estimation
Issue Date: 2010
Abstract: In digital communication receivers, ambiguities in terms of timing and phase need to be resolved prior to data detection. In the presence of powerful error-correcting codes, which operate in low signal-to-noise ratios (SNR), long training sequences are needed to achieve good performance. In this contribution, we develop a new class of code-aided ambiguity resolution algorithms, which require no training sequence and achieve good performance with reasonable complexity. In particular, we focus on algorithms that compute the maximum-likelihood (ML) solution (exactly or in good approximation) with a tractable complexity, using a factor-graph representation. The complexity of the proposed algorithm is discussed and reduced complexity variations, including stopping criteria and sequential implementation, are developed. © 2010 IEEE.
URI: https://ir.swu.ac.th/jspui/handle/123456789/14642
https://www.scopus.com/inward/record.uri?eid=2-s2.0-78649239416&doi=10.1109%2fTSP.2010.2068291&partnerID=40&md5=b2325003ededa2e647284db0c692d48d
ISSN: 1053587X
Appears in Collections:Scopus 1983-2021

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