Elements of information theory
Elements of information theory
A view of the EM algorithm that justifies incremental, sparse, and other variants
Proceedings of the NATO Advanced Study Institute on Learning in graphical models
The Journal of Machine Learning Research
Convergence Theorems for Generalized Alternating Minimization Procedures
The Journal of Machine Learning Research
Carrier phase and frequency estimation for pilot-symbol assisted transmission: bounds and algorithms
IEEE Transactions on Signal Processing
The Variational Inference Approach to Joint Data Detection and Phase Noise Estimation in OFDM
IEEE Transactions on Signal Processing
On LDPC codes over channels with memory
IEEE Transactions on Wireless Communications
Unified design of iterative receivers using factor graphs
IEEE Transactions on Information Theory
Algorithms for iterative decoding in the presence of strong phase noise
IEEE Journal on Selected Areas in Communications
Code-aided maximum-likelihood ambiguity resolution through free-energy minimization
IEEE Transactions on Signal Processing
Recursive inference for inverse problems using variational Bayes methodology
Proceedings of the 5th International ICST Conference on Performance Evaluation Methodologies and Tools
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The problem of iterative detection/decoding of data symbols transmitted over an additive white Gaussian noise (AWGN) channel in the presence of phase uncertainty is addressed in this paper. By modelling the phase uncertainty either as an unknown deterministic variable/process or random variable/process with a known a priori probability density function, a number of non-Bayesian and Bayesian detection algorithms with various amount of suboptimality have been proposed in the literature to solve the problem. In this paper, a new set of suboptimal iterative detection algorithms is obtained by utilizing the variational bounding technique. Especially, applying the generic variational Bayesian (VB) framework, efficient iterative joint estimation and detection/decoding schemes are derived for the constant phase model as well as for the dynamic phase model. In addition, the relation of the VB-based approach to the optimal noncoherent receiver as well as to the classical approach via the expectation-maximization (EM) algorithm is provided. Performance of the proposed detectors in the presence of a strong dynamic phase noise is compared to the performance of the existing detectors. Furthermore, an incremental scheduling of the VB (or EM) algorithm is shown to reduce the overall complexity of the receiver.