Joint Channel and Frequency Offset Estimation for Oversampled Perfect Reconstruction Filter Bank Transceivers

Recently, DFT-based oversampled perfect reconstruction filter banks (OPRFB), as a special form of filtered multitone, have shown great promises for applications to multicarrier modulation. Still, accurate frequency synchronization and channel equalization are needed for their reliable operation in practical scenarios. In this paper, we first derive a data-aided joint maximum likelihood (ML) estimator of the carrier frequency offset (CFO) and the channel impulse response (CIR) for OPRFB transceiver systems operating over frequency selective fading channels. Then, by exploiting the structural and spectral properties of these systems, we are able to considerably reduce the complexity of the proposed estimator through simplifications of the underlying likelihood function. The Cramer Rao bound on the variance of unbiased CFO and CIR estimators is also derived. The performance of the proposed ML estimator is investigated by means of numerical simulations under realistic conditions with CFO and frequency selective fading channels. The effects of different pilot schemes on the estimation performance for applications over time-invariant and mobile time-varying channels are also examined. The results show that the proposed joint ML estimator exhibits an excellent performance, where it can accurately estimate the unknown CFO and CIR parameters for the various experimental setups under consideration.