Proc. IEEE Global Communications Conference, Nov. 27-Dec 1, 2006, accepted for publication.

Low-Complexity Adaptive High-Resolution Channel Prediction for 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 -

Paper Draft - Slides

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 frequency. The algorithm is based on an improved rank and subspace adaptive Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT). 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.