Asymptotic Cramér-Rao Bounds and Training Design for MIMO-OFDMA Uplink Transmissions with Frequency Offsets

In this paper, we address the data-aided joint estimation of frequency offsets and channel coefficients in the uplink transmission of MIMO-OFDMA systems. A compact and informative expression of the Cramér-Rao Bound (CRB) is derived for large training sequence sizes. It is shown that the asymptotic performance bounds do not depend on the number of users and the values of the frequency offsets. Moreover, we observe that different carrier assignment schemes (CAS) may lead to the same asymptotic CRB. Next, we bring to the fore the training strategies which minimize the asymptotic performance bounds and which are therefore likely to lead to accurate estimates of the parameters. In particular, for a given user, it is shown that accurate frequency offset estimates are likely to be obtained by introducing relevant correlation between training sequences sent at different antennas. On the otherhand, accurate channel estimation is achieved when training sequences sent at different antennas are uncorrelated. Simulation results sustain our claims.