reduced complexity data-aided and code-aided frequency offset estimation for flat-fading MIMO channels

This contribution deals with carrier frequency offset estimation for flat-fading multiple-input multiple-output (MIMO) channels. Both data-aided (using training symbols) and iterative code-aided (using the unknown coded symbols) estimation is considered. In both scenarios, maximum-likelihood (ML) frequency offset estimation involves solving a maximization problem with no closed-form solution. Since numerical calculation of the ML estimates is computationally hard, we derive a simple closed-form approximation. Simulation results indicate that the ML algorithm and the proposed reduced-complexity algorithm operate closely to the Cramer-Rao bound (CRB)