IEEE Transactions on Signal Processing,
vol. 56, no. 4, Apr. 2008, pp. 1601-1615.
Sinusoidal Modeling and Adaptive Channel Prediction in
Mobile OFDM Systems
Ian C. Wong and
Brian L. Evans
Department of Electrical and Computer Engineering,
Engineering Science Building,
The University of Texas at Austin,
Austin, TX 78712-1084 USA
OFDM Research at UT Austin
We propose a wireless fading channel prediction algorithm for a pilot-symbol
aided Orthogonal Frequency Division Multiplexing (OFDM) system.
Assuming a doubly selective (time and frequency varying) ray-based physical
channel model and equispaced pilot subcarriers in time and frequency, this
algorithm performs channel model parameter acquisition using a 2-Step 1D
ESPRIT (estimation of signal parameters via rotational invariance techniques)
as a first stage, and channel prediction via model extrapolation as a second
Since the channel model parameter acquisition has cubic complexity, we
also propose a linear complexity channel parameter tracking algorithm
based on an improved adaptive ESPRIT algorithm to continuously adapt to
the time-varying channel model parameters.
We derive the Cramer-Rao Lower Bound (CRLB) and Asymptotic CRLB (ACRLB)
for the mean squared error (MSE) in OFDM channel prediction.
We show that our proposed OFDM channel prediction algorithm has better
MSE performance while maintaining similar computational complexity than
previous methods in sparse multipath fading channels characterized by
specular scattering, which is most suitable for outdoor mobile macro-cell
Thus, our method can be seen as a complement to the existing schemes that
are more suitable for dense multipath channels with diffuse scattering,
which is typical of urban pico-cell and indoor wireless scenarios.
We provide simulation results based on the IEEE 802.16e mobile broadband
wireless access standard to corroborate our claims.
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