Channel Estimation and Prediction for Adaptive OFDM Downlinks.

Mikael Sternad and Daniel Aronsson, Uppsala University

IEEE Vehicular Technology Conference VTC2003-Fall, Orlando, FLA, Oct. 2003. © IEEE

Outline:

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, without unneccesary 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.

Abstract:

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.

Related publications:

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).

Case Study on the Wiener LMS adaptation algorithm (IEEE TCOM).

Design of the general constant-gain adaptation algorithms (IEEE SP).

Analysis of stability and performance (IEEE SP).

Source:

Pdf, (435K)
Postscript (456K)
Poster in Pdf, (2264K)