Maximum likelihood estimation of carrier frequency offset in correlated MIMO OFDM systems

This paper discusses maximum likelihood (ML) carrier frequency offset (CFO) estimation based on virtual subcarriers for multiple-input multiple-output (MIMO) systems employing orthogonal frequency division multiplexing (OFDM) over Rayleigh fading channels. In our ML approach, the channel and data are treated as random variables, unlike existing ML approaches in which the channel and data are treated as unknown constants. This in turn enables us to incorporate the spatial correlation and transmit data correlation into the analysis. In particular, we derive closed-form cost functions which can be used to accurately estimate the CFO. We also derive the Cramér-Rao lower bounds (CRLBs) for these estimators. We show that the presence of these correlations does not impact the CFO estimation significantly, especially at high signal-to-noise ratio. We present several examples to support the analysis.