An SVM training-based weight coefficient migration improved Volterra filter equalization method

Abstract

The invention relates to an SVM training-based weight coefficient migration improved Volterra filter equalization method, which comprises the following steps: S1, demodulating an optical signal transmitted by an optical fiber, and extracting a training sequence from a received signal of a receiving end; s2, constructing a feature vector for the training sequence according to the structure of the Volterra filter, and constructing a training set; s3, inputting the training set into an SVM trainer, and obtaining an optimal hyperplane through calculation; s4, extracting a normal vector of the optimal hyperplane as a weight coefficient, and migrating the weight coefficient to a Volterra filter as a tap coefficient; and S5, inputting a signal sequence to be detected into the Volterra filter in the S4, and judging the output of the Volterra filter to realize channel equalization. According to the invention, the normal vector of the optimal hyperplane is migrated to the Volterra filter, the tap coefficient of the filter does not need to be updated by an adaptive algorithm, and the calculation complexity is reduced.

Description

technical field [0001] The invention belongs to the technical field of optical communication, and in particular relates to an improved Volterra filter equalization method based on weight coefficient migration of SVM training, which is applied to a high-speed optical fiber transmission system. Background technique [0002] In recent years, with the continuous emergence of emerging services and devices such as smart handheld terminal devices, ultra-high-definition video TVs, big data cloud storage, cloud computing, and virtual reality, the demand for access bandwidth of various terminal devices on the user side continues to increase, and at the same time It relieves the pressure on the access end of short-distance optical fiber communication systems such as optical fiber access networks and data centers. This puts forward new requirements for the optical fiber transmission system in terms of bandwidth, network capacity, business support capability and overall performance. The...

AI Technical Summary

Problems solved by technology

Among them, the equalizer (VE) based on the Volterra filter is widely used, but it is limited by the difficulty of system implementation and computational complexity, especially the second-order and third-order VE requires dozens or even hundreds of taps to achieve Satisfactory performance, greatly increasing the implementation complexity of the system

At present, there are few programs based on machine learning algorithms for performance improvement of DML chirp effect and nonlinear interference.

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