Radius guidance Kalman based parameter adaptive polarization state tracking and equalization method

Abstract

The invention provides a radius guidance Kalman based parameter adaptive polarization state tracking and equalization method. The radius guidance Kalman based parameter adaptive polarization state tracking and equalization method comprises the steps of S1, performing polarization state tracking and channel equalization; and S2, performing adaptive Kalman filtering algorithm, wherein each iterativeupdate of the algorithm is divided into a prediction stage and an update stage; in the prediction stage, the adaptive Kalman filtering algorithm is to predict filter parameters at the current momentbased on a priori value; in the update stage, the adaptive Kalman filtering algorithm is to calculate an error function based on a radius guidance way, perform multiple Kalman updates through a methodof ariational bayesian approximations, adjust noise covariance and correct filter parameters; and the correction value is a priori value of the next moment. The radius guidance Kalman based parameteradaptive polarization state tracking and equalization method has the beneficial effects that the algorithm stability is greatly improved and the optical communication stability is improved.

Description

technical field [0001] The invention relates to the field of optical communication, in particular to a radius-guided Kalman-based parameter adaptive polarization state tracking and equalization method. Background technique [0002] In the field of optical communication, polarization multiplexing (PDM) technology can double the capacity of the system without changing the backbone communication link, but requires the receiving end to be able to effectively track the polarization state of the optical signal. In a new generation of coherent optical communication systems, digital signal processing algorithms are often used to achieve fast and robust polarization tracking and channel equalization. Traditional equalization algorithms have slow convergence speed and tracking speed, such as common touch algorithm (CMA), multimode algorithm (MMA), and radius-directed equalizer (RDE). The algorithm based on Kalman filter is proposed for fast polarization state tracking and channel equ...

AI Technical Summary

Problems solved by technology

The covariance matching method is widely used due to its low computational complexity, but it is not accurate enough

Bayesian method is the most accurate adaptive algorithm, and several other methods can be considered as its approximate scheme, but the complexity is higher

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