Details of Research Outputs

In this paper, a robust, dynamic and joint carrier frequency offset (CFO) and linear phase noise (LPN) tracking algorithm is proposed by utilizing the H∞ filter for CO-OFDM systems. The dynamic tracking is implemented by Bayesian filtering which time-recursively updates the posterior state estimates based on the prior state information and calculated gain. To improve the robustness of the dynamic tracking process against the uncertainty of the noise terms, we adopt the H∞ filter, which designs a min-max optimization problem with a performance bound-defined constraint. The H∞ filter obtains the state estimates by limiting the worst-case estimation error, which guarantees its robustness to the system uncertain noise terms and external disturbances. After the joint estimation of CFO and LPN, we then propose a decision-feedback algorithm to achieve accurate signal detection. The accuracy and robustness of the proposed algorithm are verified in an 88.9 Gb/s 16-QAM CO-OFDM system, by comparing with the conventional extended Kalman filter (EKF) and Gaussian particle filter (GPF). Simulation results show that the MSEs of CFO and LPN by H∞ filter can reach the Cramér-Rao Lower Bounds (CRLBs) as well as the Bayesian CRLB (BCRLB), and possesses excellent noise and chromatic dispersion (CD) tolerance.