Tracking of Time-varying Mobile Radio Channels,
Part I: The Wiener LMS Algorithm.
IEEE Transactions on Communication,
December 2001, pp.2207-2217.
A brief version of the paper,
"Channel Tracking with WLMS Algorithms:
High Performance at LMS Computational Loads"
A. Ahlén, L. Lindbom and M. Sternad,
is published at
IEEE Vehicular Technology Conference -VTC200-Spring
Tokyo, Japan, May 15-18, 2000, pp 16-20.
We here propose a novel way of extending and
optimizing the structure of LMS-like adaptation laws.
These results were originally motivated by the
difficult problem of accurately tracking rapidly
time-varying channel parameters in the
IS-136 cellular system. An
of the proposed algorithm
has successfully been used
on IS-136 1900MHz channels
and a case study on this particular application
can be found in
Part II of this work.
Motivated also by other applications such as
multi-carrier systems and
a framework has been developed
for designing low-complexity algorithms for tracking
coefficients of linear regression models
under assumptions which are
realistic in communications
applications. Our aim is to improve upon the
sometimes inadequate tracking performance
offered by standard LMS and RLS algorithms.
Adaptation algorithms with constant
gains are designed
for tracking smoothly time-varying
parameters of linear
in particular channel models
occurring in mobile radio communications.
In a companion paper, an application
to channel tracking
in the IS-136 TDMA system is discussed.
The proposed algorithms are based on
two key concepts.
First, the design is transformed into a
Wiener filtering problem. Second,
the parameters are modeled as correlated
ARIMA processes with known dynamics.
This leads to a new framework for
systematic and optimal design of simple
adaptation laws based on a priori information.
They can be realized as Wiener filters, called
Learning Filters, or as ``LMS/Newton'' updates
complemented by filters that provide
predictions or smoothing estimates.
The simplest algorithm,
named Wiener LMS, is presented here.
All parameters are here assumed
governed by the same
dynamics and the covariance matrix of the regressors
is assumed known. The computational
complexity is of the same order of magnitude as
that of LMS for white
regressors. The tracking performance
is however substantially improved.
Part II: A Case Study
on IS-136 channels.
of the general constant-gain adaptation algorithms.
of stability and performance, for slow and fast variations.
PhD Thesis by Lars Lindbom.
Licenciate Thesis by Lars Lindbom,
on averaged Kalman designs (KLMS)
and on deterministic sinusoid modelling of fading channels.
Conference paper (IFAC Como 2001)
on averaged robust design for uncertain fading models.
Conference version (VTC2000S):
Postscript, 195K ;
Paper version (IEEE-COM):
Postscript, 306K ;
With this file, Wiener LMS adaptation law can be designed
if ARIMA models of the parameters to be tracked are given.
Function called by wlms_design.m
Function called by wlms_design.m.
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