Digital Communication Receivers: Synchronization, Channel Estimation, and Signal Processing
Digital Communication Receivers: Synchronization, Channel Estimation, and Signal Processing
Belief Optimization for Binary Networks: A Stable Alternative to Loopy Belief Propagation
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Iterative code-aided ML phase estimation and phase ambiguity resolution
EURASIP Journal on Applied Signal Processing
Adaptive iterative detectors for phase-uncertain channels via variational bounding
IEEE Transactions on Communications
IEEE Transactions on Signal Processing
The Variational Inference Approach to Joint Data Detection and Phase Noise Estimation in OFDM
IEEE Transactions on Signal Processing
MAP-Based Code-Aided Hypothesis Testing
IEEE Transactions on Wireless Communications
The generalized distributive law
IEEE Transactions on Information Theory
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
Unified design of iterative receivers using factor graphs
IEEE Transactions on Information Theory
Constructing free-energy approximations and generalized belief propagation algorithms
IEEE Transactions on Information Theory
A Divergence Minimization Approach to Joint Multiuser Decoding for Coded CDMA
IEEE Journal on Selected Areas in Communications
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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.