We propose a non-data-aided phase noise compensation technique that overcomes the limitation of the zerooverhead decision-directed (DD) phase noise suppression method in high-order constellations for coherent optical orthogonal frequency division multiplexing (CO-OFDM) systems. The proposed technique consists of two equalization stages. First, a blind adaptive phase offset recovery scheme based on high-order statics, called the dispersion minimization de-rotator, is applied to subcarrier symbols to achieve rough correction of the common phase error because of phase noise. Second, the conventional DD phase noise correction scheme is used to remove the residual common phase error. The proposed method effectively compensates phase noise without the help of pilot signals, especially for high-order constellation CO-OFDM systems, as confirmed by our simulation results.Index Terms-Phase noise, optical orthogonal frequency division multiplexing, coherent communication, adaptive phase noise mitigation.

We propose a blind feedforward phase noise mitigation method in the time-domain for a coherent optical orthogonal frequency division multiplexing (CO-OFDM) systems. By exploiting the redundancy of the cyclic prefix (CP), the proposed scheme acquires the overall phase noise difference information during an OFDM block and attempts to mitigate the phase noise in the time domain using a linear approximation. The proposed algorithm mitigates common phase error (CPE) and inter-carrier-interference (ICI) due to phase noise simultaneously, improving the system performance, especially when decision-directed equalizers are used. The simulation results demonstrate the effectiveness of the proposed feedforward phase noise mitigation approach in time domain.

Transmitter in-phase and quadrature (Tx IQ) mismatch is one of the major obstacles preventing coherent optical orthogonal frequency division multiplexing systems from achieving high data rates. Several Tx IQ mismatch compensation methods at the receiver side have been proposed based on the mismatch estimation by the use of pilot signals. In this letter, we propose a novel pilot system for the Tx IQ mismatch estimation to improve the channel-estimation performance. The pilot scheme exploits the conjugate redundancy of the subcarrier mirror images due to the Tx IQ mismatch and allows the estimation of the Tx IQ mismatch factor and channel parameter independently. The advantages of the proposed algorithm are proved via mean square error analysis, and the bit error rate improvement is shown in simulations.Index Terms-Coherent communication, in-phase and quadrature mismatch, optical orthogonal frequency division multiplexing.

We propose a reduced complexity maximum likelihood sequence detection (MLSD) equalizer for large size constellation signals such as 8-VSB using bidirectional decision feedback equalizers (DFEs). We apply reduced-length two-level Viterbi algorithm to select one of two non-identical symbol estimates produced by a bidirectional DFE. Therefore, the computationally expensive MLSD algorithm is applied sparingly for two-level signals with the effective channel length shorter than the original channel, regardless of the original constellation size of the symbols. Simulations results show that the proposed algorithm outperforms existing combination schemes based on bidirectional DFEs, especially for large size constellation.

We propose a novel reduced-state maximum-likelihood sequence detection (MLSD) structure using the Viterbi algorithm based on the second-order Volterra kernel modeling nonlinear distortion due to square law detection of multipath channels commonly occurring in chromatic dispersion (CD) or polarization mode dispersion (PMD) in optical communication systems. While all existing MLSD methods for square-law detection receivers are based on direct computation of branch metrics, the proposed algorithm provides an efficient and structured way to implement reduced-state MLSD with almost the same complexity of a MLSD for linear channels. As a result, the proposed algorithm reduces the number of parameters to be estimated and the complexity of computation.