Intelligent adaptive equalizer and equalization demodulation method based on machine learning

Abstract

The invention discloses an intelligent adaptive equalizer and an equalization demodulation method based on machine learning. The method includes the following steps: the data of an acquired signal ispreprocessed, and the energy of input data is normalized. Then, the cluster groups in the data are clustered by using a Gaussian kernel function and a distance function under the condition of withoutany other prior knowledge, and the cluster groups in the clustered data are labeled by using the nearest neighbor algorithm in order to realize useful informationization of modulation signals. The discrete noise points outside the cluster groups are not clustered, and the cluster halos without clustering labels are marked by using the weighted K-nearest neighbor algorithm. Finally, the data is allintegrated to obtain an overall label, and the error rate of the system is estimated by comparing the overall label with pre-stored labels. By using the method of the invention, the real clustering center can be identified without any other prior knowledge, regardless of the shape and size. The computational complexity is reduced, and the accuracy of the classification result is significantly improved. The method can adapt to most of the modulation formats in the current communication system.

Description

technical field [0001] The invention relates to an intelligent coherent optical fiber communication method, in particular to an intelligent adaptive algorithm used in a coherent optical communication system. Background technique [0002] With the increase of various bandwidth-intensive applications such as big data, cloud services, smartphones and network video, network traffic is growing rapidly. Cognitive optical network (CON) can provide automatic configuration, self-optimization and autonomous network operation through self-learning and cognitive decision-making, and can also coordinate transmitters and receivers to realize real-time dynamic adjustment of modulation format, line rate and spectrum allocation, etc. It has better scalability and interoperability. Through coordinated management of transceivers, link functions such as intelligent channel management and bandwidth allocation of network nodes can be realized, thereby improving service quality and transmission q...

AI Technical Summary

Problems solved by technology

However, different signal modulation formats require different equalization and demodulation algorithms, which wastes hardware resources to a certain extent.

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