Time-Variant Channel Prediction using Time-Concentrated and Band-Limited Sequences

Abstract

We present the basic methodology for minimum-energy bandlimited prediction of time-variant flat Rayleigh-fading channels. This predictor is based on a subspace spanned by time-concentrated and bandlimited sequences. The concept of time-variant channel estimation using time-concentrated and band-limited sequences was introduced recently by Zemen et al. We extend this concept to the problem of time-variant channel prediction. Slepian showed that discrete prolate spheroidal (DPS) sequences can be used to calculate the minimum-energy bandlimited continuation of a finite sequence. DPS sequences are optimal for a time-variant channel with flat Doppler spectrum. We generalize the concept of time-concentrated and band-limited sequences to arbitrary Doppler spectra approaching closely the lower prediction error limit defined by the Wiener filter. In practical systems detailed channel covariance information is not available. We design a set of subspaces spanned by DPS sequences with fixed time-concentration but growing bandwidth. The best DPS subspace is selected dynamically for each observation interval using a Doppler bandwidth estimate allowing for low complexity channel prediction. The performance of the new predictor is compared to that of the Wiener filter by means of Monte Carlo simulations.