Convergence of Markov-Chain Monte-Carlo Approaches to Multiuser and MIMO Detection

Markov-chain Monte-Carlo methods have been demonstrated to offer an attractive alternative to the design of approximate (near optimal) maximum a-posteriori (MAP) detectors for synchronous direct-sequence code-division multiple access (DS-CDMA) and multi-input, multi-output (MIMO) multiple antenna ap...

Full description

Saved in:
Bibliographic Details
Published in:IEEE journal on selected areas in communications 2008-04, Vol.26 (3), p.497-505
Main Authors: Henriksen, S., Ninness, B., Weller, S.R.
Format: Article
Language:English
Subjects:
Antennas
Approximation
Computational complexity
Convergence
Detectors
Direct-sequence code-division multiple access
MIMO
Monte Carlo methods
Multiaccess communication
Multiple access
Multiuser detection
Optimization
Random number generation
Simulation
Spread spectrum communication
Synchronous
Transmitting antennas
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Markov-chain Monte-Carlo methods have been demonstrated to offer an attractive alternative to the design of approximate (near optimal) maximum a-posteriori (MAP) detectors for synchronous direct-sequence code-division multiple access (DS-CDMA) and multi-input, multi-output (MIMO) multiple antenna applications. Central to evaluating these method is understanding their convergence properties. In other works, this has been established via simulation, and the underlying theoretical basis has been identified. The contribution of this paper is to extend the theoretical understanding by rigorously establishing both convergence and convergence rate results for a wide class of Metropolis-Hastings methods.
ISSN:0733-8716
1558-0008
DOI:10.1109/JSAC.2008.080408