Simplified Kalman Estimation of
Fading Mobile Radio Channels:
High Performance at LMS Computational Loads
IEEE International Conference on Acoustics,
Speech and Signal Processing,
Minneapolis, MN, vol III, pp 352-355, April 27-30, 1993.
© 1993 IEEE.
Paper In Pdf (with figures) 305K.
Parameters of time-varying systems are often estimated
by adaptive algorithms with sliding time-windows, which
discount old data.
We may then face a dilemma: the use of a short data window
(or, equivalently, a large adaptation gain) results in noisy estimates.
With a long data window (small gain), time varying parameters
are tracked with a considerable delay.
To improve the accuracy, the present paper suggests
a low-complexity algorithm which takes
a priori information about the
properties of the time-variations into account,
in the form of stochastic models.
Low complexity algorithms for channel estimation in
Rayleigh fading environments are presented.
The channel estimators are presumed to operate in
conjunction with a Viterbi detector, or an equalizer.
The algorithms are based on simplified internal
modelling of time-variant channel coefficients and
approximation of a Kalman estimator.
A novel averaging approach is used to replace the on-line
update of the Riccati equation with a constant matrix.
The associated Kalman gain is expressed in an
Compared to RLS tracking, both a significantly lower bit
error rate and a much lower computational complexity
- Related publications:
by L. Lindbom, with more details on the algorithm.
of time-varying channel coefficients in IS-136 systems.
A series of four papers outlining the later development of a
complete design methodology, based on stochastic models
of time-varying parameters:
of general constant-gain adaptation algorithms.
Part II: Analysis
of stability and performance, for slow and fast variations.
The Wiener LMS
adaptation algorithm, a special case with low complexity.
A Case Study on IS-136 1900MHz channels.
- PhD Thesis
by L. Lindbom 1995, presenting the general design
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