This paper addresses the problem of carrier frequency synchronization and time-varying channel equalization in the uplink of a broadband multi-user wireless communication system employing an oversampled perfect reconstruction filter bank (OPRFB) transceiver structure for multi-carrier modulation. Based on the maximum likelihood (ML) principle, a pilot-aided joint estimator of the carrier frequency offsets (CFO) and channel equalizer coefficients of the multiple users is proposed. The performance of the new estimator is examined for various subband allocation schemes by means of numerical simulations under realistic conditions of operation. For mobile users with time-varying fading channels, we also study the effect of using different distributions of pilots over time. Our results show that the proposed estimator can provide accurate estimates of the unknown CFO and channel parameters, which in turn can be used to design effective compensation mechanisms.

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.