Iterative channel estimation and data detection in fast fading channels

Abstract

Iterative joint channel estimation and data detection/decoding has been shown to provide good performance in slow fading channels. In fast fading channels, the previously proposed techniques require a significant amount of the transmitted signal to be dedicated to non-data carrying pilot symbols reducing system efficiency. This paper proposes a channel estimation system using an extended Kalman filter to provide an initial estimate of the channel gains which is smoothed using a Discrete Prolate Spheroidal sequence basis expansion model technique. The smoothed channel gain estimates are then fed into a Kalman filter equalizer to provide an improved data signal estimate for calculation of the probabilistic information of the transmitted data for decoding purposes. This technique allows for BER performance within 1 dB of when ideal channel state information is available even when the Doppler frequency is as high as one percent of the data symbol rate. In addition, the proposed technique requires a smaller proportion of pilot symbols to data symbols for acceptable error performance compared to previously proposed methods.

A novel channel estimation method based on Kalman filter compressed sensing for time-varying OFDM system

“…The value of a and the variance of the process noise σ 2 v can be obtained through the Yule-Walker equation [16]. They are expressed as…”