Nonlinear Prediction of Mobile Radio Channels: Measurements and
MARS Model Designs.
Univ. of Vienna
IEEE International Conference on Acoustics,
Speech and Signal Processing,
March 15-19, 1999, pp 2667-2670. © 1999 IEEE
The rapid time variation of mobile radio channels is often modeled as
a random process with second order moments reflecting vehicle
speed, bandwidth and the scattering environment. These statistics
typically show that there is little room for prediction of channel
properties such as received power or complex taps of the impulse
coefficients, at least when linear predictor structures are
We use mutual information estimation to measure
statistical dependencies in sequences of wideband mobile radio channel
data and find significant nonlinear dependencies, far exceeding the
component. Based on these upper limits for the predictability of
channel evolution over time intervals up to 30 ms ahead, we develop
practical nonlinear predictor systems using Multivariate Adaptive
Regression Splines (MARS).
We demonstrate computationally efficient
schemes that increase the prediction horizon beyond 10 ms, compared
to less than 4 ms with linear predictors at comparable prediction gains.
by Torbjörn Ekman.
PhD Thesis on adaptive algorithms
providing linear prediction estimates.
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