2,399 research outputs found
Chaos multiculturel ou utopie urbaine, la ville plurilingue dans le cinéma de science-fiction de Métropolis à Élysium
International audienc
Merging Belief Propagation and the Mean Field Approximation: A Free Energy Approach
We present a joint message passing approach that combines belief propagation
and the mean field approximation. Our analysis is based on the region-based
free energy approximation method proposed by Yedidia et al. We show that the
message passing fixed-point equations obtained with this combination correspond
to stationary points of a constrained region-based free energy approximation.
Moreover, we present a convergent implementation of these message passing
fixedpoint equations provided that the underlying factor graph fulfills certain
technical conditions. In addition, we show how to include hard constraints in
the part of the factor graph corresponding to belief propagation. Finally, we
demonstrate an application of our method to iterative channel estimation and
decoding in an orthogonal frequency division multiplexing (OFDM) system
Message-Passing Algorithms for Channel Estimation and Decoding Using Approximate Inference
We design iterative receiver schemes for a generic wireless communication
system by treating channel estimation and information decoding as an inference
problem in graphical models. We introduce a recently proposed inference
framework that combines belief propagation (BP) and the mean field (MF)
approximation and includes these algorithms as special cases. We also show that
the expectation propagation and expectation maximization algorithms can be
embedded in the BP-MF framework with slight modifications. By applying the
considered inference algorithms to our probabilistic model, we derive four
different message-passing receiver schemes. Our numerical evaluation
demonstrates that the receiver based on the BP-MF framework and its variant
based on BP-EM yield the best compromise between performance, computational
complexity and numerical stability among all candidate algorithms.Comment: Accepted for publication in the Proceedings of 2012 IEEE
International Symposium on Information Theor
Uncoordinated and Decentralized Processing in Extra-Large MIMO Arrays
We propose a decentralized receiver for extra-large multiple-input
multiple-output (XL-MIMO) arrays. Our method operates with no central
processing unit (CPU) and all the signal detection tasks are done in
distributed nodes. We exploit a combined message-passing framework to design an
uncoordinated detection scheme that overcomes three major challenges in the
XL-MIMO systems: computational complexity, scalability and non-stationarities
in user energy distribution. Our numerical evaluations show a significant
performance improvement compared to benchmark distributed methods while
operating very close to the centralized receivers.Comment: 14 pages, 3 figure
A Deep Learning Approach to Location- and Orientation-aided 3D Beam Selection for mmWave Communications
Position-aided beam selection methods have been shown to be an effective
approach to achieve high beamforming gain while limiting the overhead and
latency of initial access in millimeter wave (mmWave) communications. Most
research in the area, however, has focused on vehicular applications, where the
orientation of the user terminal (UT) is mostly fixed at each position of the
environment. This paper proposes a location- and orientation-based beam
selection method to enable context information (CI)-based beam alignment in
applications where the UT can take arbitrary orientation at each location. We
propose three different network structures, with different amounts of trainable
parameters that can be used with different training dataset sizes. A
professional 3-dimensional ray tracing tool is used to generate datasets for an
IEEE standard indoor scenario. Numerical results show the proposed networks
outperform a CI-aided benchmark such as the generalized inverse fingerprinting
(GIFP) method as well as hierarchical beam search as a non-CI-based approach.
Moreover, compared to the GIFP method, the proposed deep learning-based beam
selection shows higher robustness to different line-of-sight blockage
probability in the training and test datasets and lower sensitivity to
inaccuracies in the position and orientation information.Comment: 30 pages, 12 figure. This article was submitted to IEEE Transactions
on Wireless Communications on Oct 11 202
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