Publication: Code-aided maximum-likelihood ambiguity resolution through free-energy minimization
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Issued Date
2010
Resource Type
File Type
application/pdf
ISSN
1053587X
Other identifier(s)
2-s2.0-78649239416
Rights Holder(s)
Scopus
Bibliographic Citation
IEEE Transactions on Signal Processing. Vol 58, No.12 (2010), p.6238-6250
Suggested Citation
Herzet C., Woradit K., Wymeersch H., Vandendorpe L. Code-aided maximum-likelihood ambiguity resolution through free-energy minimization. IEEE Transactions on Signal Processing. Vol 58, No.12 (2010), p.6238-6250. doi:10.1109/TSP.2010.2068291 Retrieved from: https://hdl.handle.net/20.500.14740/7493
Author(s)
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.
Subject(s)
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
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