Pilot symbol assisted modulation in frequency selective fading wireless channels

Frequency-selective fading channels are typically modeled either as a combi nation of Doppler components or as lowpass stochastic processes. In both ca ses, accurate parameter and/or Doppler frequency estimation is impeded by t he fact that the Doppler frequencies are typically very low (compared with the data rate) and closely spaced. This problem is mitigated in pilot symbo l assisted modulation (PSAM) systems that employ distributed training. Thos e systems can provide information about a time-undersampled version of the channel that may be easier to identify. In this paper, we address the probl em of estimating the fading channel's correlation matrices from the receive d data by exploiting the distributed training symbols. Multichannel autoreg ressive (AR) models are estimated to fit the channel's variations, and the Doppler frequencies are identified through the peaks of the AR spectrum. Th e performance of the proposed methods is studied through analytical and exp erimental results. Finally, Kalman filtering ideas are employed to track th e time-varying channel taps based on the estimated AR model.