On the convergence of the coordinate descent method for convex differentiable minimization
Journal of Optimization Theory and Applications
Graphical models for machine learning and digital communication
Graphical models for machine learning and digital communication
Introduction to Monte Carlo methods
Proceedings of the NATO Advanced Study Institute on Learning in graphical models
A view of the EM algorithm that justifies incremental, sparse, and other variants
Learning in graphical models
An Introduction to Variational Methods for Graphical Models
Machine Learning
Multiuser Detection
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
A Comparison of Algorithms for Inference and Learning in Probabilistic Graphical Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Impact of channel estimation errors on multiuser detection via the replica method
EURASIP Journal on Wireless Communications and Networking - Special issue on advanced signal processing algorithms for wireless communications
Graphical Models, Exponential Families, and Variational Inference
Foundations and Trends® in Machine Learning
The Variational Inference Approach to Joint Data Detection and Phase Noise Estimation in OFDM
IEEE Transactions on Signal Processing
IEEE Transactions on Information Theory
Large system performance of linear multiuser receivers in multipath fading channels
IEEE Transactions on Information Theory
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
Iterative multiuser joint decoding: unified framework and asymptotic analysis
IEEE Transactions on Information Theory
A statistical-mechanics approach to large-system analysis of CDMA multiuser detectors
IEEE Transactions on Information Theory
Randomly spread CDMA: asymptotics via statistical physics
IEEE Transactions on Information Theory
Constructing free-energy approximations and generalized belief propagation algorithms
IEEE Transactions on Information Theory
Bit-Level Equalization and Soft Detection for Gray-Coded Multilevel Modulation
IEEE Transactions on Information Theory
Turbo-MIMO for wireless communications
IEEE Communications Magazine
Iterative decoding of compound codes by probability propagation in graphical models
IEEE Journal on Selected Areas in Communications
Successive interference cancellation with SISO decoding and EM channel estimation
IEEE Journal on Selected Areas in Communications
| Hi-index | 754.84 |
We propose a unified framework for deriving and studying soft-in soft-out (SISO) detection in multiple-access channels using the concept of variational inference. The proposed framework may be used in multiple-access interference (MAI), intersymbol interference (ISI), and multiple-input multiple-output (MIMO) channels. Without loss of generality, we will focus our attention on turbo multiuser detection, to facilitate a more concrete discussion. It is shown that, with some loss of optimality, variational interence avoids the exponential complexity of a posteriori probability (APP) detection by optimizing a closely related, but much more manageable, objective function called variational free energy. In addition to its systematic appeal, there are several other advantages to this viewpoint. First of all, it provides unified and rigorous justifications for numerous detectors that were proposed on radically different grounds, and facilitates convenient joint detection and decoding (utilizing the turbo principle) when error-control codes are incorporated. Second, efficient joint parameter estimation and data detection is possible via the variational expectation maximization (EM) algorithm, such that the detrimental effect of inaccurate channel knowledge at the receiver may be dealt with systematically. We are also able to extend BPSK-based SISO detection schemes to arbitrary square QAM constellations in a rigorous manner using a variational argument.