Pilot symbol assisted modulation in frequency selective fading wireless channels

Frequency-selective fading channels are typically modeled either as a combination of Doppler components or as lowpass stochastic processes. In both cases, accurate parameter and/or Doppler frequency estimation is impeded by the fact that the Doppler frequencies are typically very low (compared with the data rate) and closely spaced. This problem is mitigated in pilot symbol assisted modulation (PSAM) systems that employ distributed training. Those systems can provide information about a time-undersampled version of the channel that may be easier to identify. We address the problem of estimating the fading channel's correlation matrices from the received data by exploiting the distributed training symbols. Multichannel autoregressive (AR) models are estimated to fit the channel's variations, and the Doppler frequencies are identified through the peaks of the AR spectrum. The performance of the proposed methods is studied through analytical and experimental results. Finally, Kalman filtering ideas are employed to track the time-varying channel taps based on the estimated AR model.