Proc. IEEE Global Communications Conference,
Nov. 27-Dec 1, 2006, accepted for publication.
Low-Complexity Adaptive High-Resolution Channel Prediction for
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
Paper Draft -
OFDM Research at UT Austin
We propose a low-complexity adaptive high-resolution
channel prediction algorithm for pilot symbol assisted
orthogonal frequency division multiplexing (OFDM) systems.
The algorithm is derived assuming a general time- and frequency-
selective ray-based physical channel model, wherein each ray is
parameterized by a complex amplitude, time-delay, and Doppler
The algorithm is based on an improved rank and subspace adaptive
Estimation of Signal Parameters via Rotational Invariance
The adaptive ESPRIT is used to efficiently extract the slowly
varying time-delays and Doppler frequencies of each ray, followed
by a simple rotational update to compute the complex amplitudes.
Our algorithm has a principal computational complexity that is
linear in the number of pilot subcarriers used for prediction,
in contrast to cubic complexity required for a non-adaptive block
processing based algorithm.
We compare our approach with a previously proposed adaptive
OFDM channel prediction algorithm based on standard least
mean square (LMS) and recursive least squares (RLS) adaptive
filters, and show that our algorithm achieves lower mean square
error at a comparable computational complexity.
We provide simulation results based on the IEEE 802.16e standard.
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Last Updated 07/21/06.