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Flexible decision-aided maximum likelihood phase estimation in coherent optical phase-shift-keying systems

机译:相干光相移键控系统中灵活的决策辅助最大似然相位估计

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摘要

Decision-aided maximum likelihood (DA-ML) phase estimation has been applied in coherent optical communication systems due to its high computational efficiency. However, conventional DA-ML scheme only assumes constant phase noise within each observation block, thus causing block length effect (BLE) which degrades system performance. In this paper, we take into account the time-varying laser phase noise and propose a flexible DA-ML phase estimation method for carrier phase recovery in coherent optical phase-shift-keying systems so as to eliminate BLE. Weighted coefficients based on ML criterion are introduced to strengthen the estimation accuracy. The statistical property of phase estimation error is derived, and the bit error rate (BER) performance is also evaluated. Numerical simulation results show that our flexible DA-ML scheme is very robust against time-varying phase noise. Compared with conventional DA-ML receiver, it can significantly reduce the phase estimation variance, improve the BER performance and increase the laser linewidth tolerance. By adopting the flexible DA-ML method with a relatively larger block length, BLE can be effectively eliminated. Thus, the BER performance can be significantly improved without carefully finding out the optimum block length or the optimum forgotten factor.
机译:决策辅助最大似然(DA-ML)相位估计由于其高计算效率而已应用于相干光通信系统中。但是,常规的DA-ML方案仅在每个观察块内假设恒定的相位噪声,因此会导致块长度效应(BLE),从而降低系统性能。在本文中,我们考虑了时变激光相位噪声,并提出了一种灵活的DA-ML相位估计方法,用于相干光相移键控系统中的载波相位恢复,以消除BLE。引入了基于ML准则的加权系数,以提高估计精度。推导了相位估计误差的统计特性,并评估了误码率(BER)性能。数值仿真结果表明,我们灵活的DA-ML方案对于时变相位噪声非常鲁棒。与传统的DA-ML接收机相比,它可以显着减小相位估计方差,提高BER性能,并增加激光线宽容限。通过采用具有较大块长度的灵活DA-ML方法,可以有效消除BLE。因此,无需仔细找出最佳块长度或最佳遗忘因素,即可显着提高BER性能。

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