Performance of ML-based carrier frequency offset estimation in CO-OFDM systems

Abstract

In coherent optical orthogonal frequency division multiplexing (CO-OFDM) systems, carrier frequency offset (CFO) causes interference, which significantly impacts the overall system performance. This paper therefore proposes an effective estimation scheme to mitigate and compensate the undesirable effect of CFO on the CO-OFDM system. The proposed scheme is based on the maximum likelihood (ML) approach, where only two long training symbols are utilized for the CFO estimation. In order to avoid the exhaustive search traditionally associated with ML schemes and to ensure low-complexity, a closed-form expression is derived and presented for the CFO estimation. The performance of the closed-form CFO estimator is compared with existing methods in the presence of polarization mode dispersion (PMD) and chromatic dispersion (CD) along the fiber channel.