CramÉR-Rao Bound and Training Sequence Selection for Mimo-Ofdma Transmissions Impaired by Frequency Offsets

In this paper, we investigate the data-aided estimation of frequency offsets and channel coefficients in the uplink transmission of MIMO-OFDMA systems. A compact and informative expression of the deterministic Cramer-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. 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 other hand, accurate channel estimation is achieved when training sequences sent at different antennas are uncorrelated

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