Channel Estimation and Prediction for Adaptive
IEEE Vehicular Technology Conference VTC2003-Fall,
Orlando, FLA, Oct. 2003. © IEEE
The Swedish Wireless IP project studies
problems that are crucial in the evolution of UMTS towards high
data rates, as well as in future 4G technologies aimed at
rapidly mobile terminals. The goal is to attain higher
througputs for packet data in particular in downlinks,
bandwidth expansion and while providing acceptable
quality of service for various classes of traffic.
At IEEE VTC-Fall 2003, we presented our
concept for an adaptive OFDM downlink
in four interrelated papers (see links below).
This is Paper 3 of the four papers.
It discusses algorithms for channel estimation
and channel prediction, and their performance.
Channel estimation and prediction algorithms
are developed and evaluated for use in
broadband adaptive OFDM downlinks
over fading channels for vehicular users.
Accurate channel estimation may be obtained
by using a combined pilot-aided and decision-directed
approach based on Kalman filtering and prediction.
The correlation properties of the channel in both
time and space are taken into account.
Kalman performance at much lower
computational complexity is attained with
recently developed constant gain adaptation laws.
We present and evaluate a state-space
realization of such an adaptation law, with
computational complexity of the order of the square
of the number of parallel tracked pilot subcarriers.
In an adaptive OFDM system, prediction of
the channel power a few milliseconds ahead
will also be required.
Frequency-domain channel estimates can
be transformed to the time domain, and
used as regressors in channel predictors
based on linear regression.
We also make a preliminary evaluation of the
direct use of complex channel prediction
in the frequency domain for channel power prediction.
Paper 1 at VTC2003,
on adaptive modulation, multiuser diversity
and channel variability within bins.
Paper 2 at VTC2003,
on the OFDM downlink and cell planning for high SIR.
Paper 4 at VTC2003,
on the impact of prediction errors on the adaptive modulation.
An overview of the Wireless IP Project (RVK02)
Channel Power Prediction,
by using unbiased predictors and
advanced regressor noise reduction (VTC 2002-Fall).
PhD Thesis on channel prediction,
by Torbjörn Ekman.
Papers on GCG and Wiener LMS adaptation laws:
The Wiener LMS
adaptation algorithm (IEEE TCOM).
on the Wiener LMS adaptation algorithm (IEEE TCOM).
of the general constant-gain adaptation algorithms (IEEE SP).
of stability and performance (IEEE SP).
Poster in Pdf,
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