Computationally efficient frequency offset estimation for flat-fading MIMO channels: performance analysis and training sequence design


This contribution deals with the carrier frequency offset estimation for a flat-fading Multiple-Input Multiple-output (MIMO) channel using a training sequence. The resulting Maximum Likelihood (ML) estimation entails solving a maximization problem with no closed-form solution. Since numerical calculation of the estimate is computationally hard, we propose a sub-optimal closed-form solution. In contrast with Single-Input-Single-Output (SISO) systems, however, self-noise arises in MIMO closed-form frequency offset estimation. Through proper training sequence design we show how to avoid this self-noise and achieve a performance close to ML-performance and the Cramer-Rao Bound (CRB).

IEEE Global Telecommunications Conference (Globecom)