On the inclusion of channel's time dependence in a hidden Markov model for blind channel estimation

The theory of hidden Markov models (HMM) is applied to the problem of blind (without training sequences) channel estimation and data detection. Within a HMM framework, the Baum-Welch (1970) identification algorithm is frequently used to find out maximum-likelihood (ML) estimates of the corresponding...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on vehicular technology 2001-05, Vol.50 (3), p.867-873
Main Authors: Anton-Haro, C., Fonollosa, J.A.R., Fauli, C., Fonollosa, J.R.
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
Blind equalizers
Blinds
Channel estimation
Channels
Computer simulation
Detection, estimation, filtering, equalization, prediction
Equipments and installations
Estimates
Exact sciences and technology
GSM
Hidden Markov models
Information, signal and communications theory
Iterative algorithms
Mathematical models
Maximum likelihood detection
Maximum likelihood estimation
Mobile radiocommunication systems
Parametric statistics
Radiocommunications
Signal and communications theory
Signal processing algorithms
Signal, noise
Telecommunications
Telecommunications and information theory
Time dependence
Time-varying channels
Training
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:The theory of hidden Markov models (HMM) is applied to the problem of blind (without training sequences) channel estimation and data detection. Within a HMM framework, the Baum-Welch (1970) identification algorithm is frequently used to find out maximum-likelihood (ML) estimates of the corresponding model. However, such a procedure assumes the model (i.e., the channel response) to be static throughout the observation sequence. By means of introducing a parametric model for time-varying channel responses, a version of the algorithm, which is more appropriate for mobile channels [time-dependent Baum-Welch (TDBW)] is derived. Aiming to compare algorithm behavior, a set of computer simulations for a GSM scenario is provided. Results indicate that, in comparison to other Baum-Welch (BW) versions of the algorithm, the TDBW approach attains a remarkable enhancement in performance. For that purpose, only a moderate increase in computational complexity is needed.
ISSN:0018-9545
1939-9359
DOI:10.1109/25.933319