EM-Based Maximum-Likelihood Channel Estimation in Multicarrier Systems With Phase Distortion

In this paper, we address channel-impulse response (CIR) estimation in multicarrier systems with phase distortion, namely, phase noise (PHN) and carrier frequency offset (CFO). The estimation problem also considers the joint estimation of the channel noise variance, CFO, and PHN bandwidth. We develo...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on vehicular technology 2013-01, Vol.62 (1), p.152-160
Main Authors: Carvajal, R., Aguero, J. C., Godoy, B. I., Goodwin, G. C.
Format: Article
Language:English
Subjects:
Applied sciences
Bandwidth
Carrier frequency offset
Channel estimation
Detection, estimation, filtering, equalization, prediction
Exact sciences and technology
expectation-maximization (EM) algorithm
Information, signal and communications theory
Joints
maximum likelihood (ML) estimation
Maximum likelihood estimation
Modulation, demodulation
multicarrier systems
Phase noise
Signal and communications theory
Signal, noise
Systems, networks and services of telecommunications
Telecommunications
Telecommunications and information theory
Training
Transmission and modulation (techniques and equipments)
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:In this paper, we address channel-impulse response (CIR) estimation in multicarrier systems with phase distortion, namely, phase noise (PHN) and carrier frequency offset (CFO). The estimation problem also considers the joint estimation of the channel noise variance, CFO, and PHN bandwidth. We develop a general state-space model for multicarrier systems, separating the complex signals into their real and imaginary parts. This provides a valid framework for any modulation scheme (proper or improper). We use the expectation-maximization (EM) algorithm to solve the maximum-likelihood (ML) estimation problem. Our approach exploits the linear and Gaussian structure associated with the transmitted signal. Due to the nonlinear nature of the PHN, sequential Monte Carlo (MC) techniques are considered. Our analysis includes general expressions under different training scenarios. We show, via simulation, the impact of PHN bandwidth estimation on overall parameter estimation, and we study the impact of different training levels. In addition, we consider the accuracy of the parameter estimates, providing expressions for the Fisher information matrix (FIM) and focusing on the estimation accuracy of the PHN bandwidth.
ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2012.2217361