Fiber nonlinear equalization method for 64-qam coherent optical communication system

Abstract

Disclosed in the present invention is an optical fiber nonlinearity equalization method used in a 64-QAM coherent light communications system, processing received 64-QAM data, and comprising: configuring a received data set to be a first-level data set, calculating a density parameter of each data point, setting a threshold, and selecting data having a density parameter exceeding the designated threshold as a second-level data set; demodulating the second-level data set, dividing into 64 clusters, and acquiring 64 centroids; according to the acquired centroids, categorizing data in the second-level data set into corresponding clusters according to a nearest Euclidean distance, and updating using centroids of 64 acquired new clusters; allocating data in the first-level data set into corresponding clusters according to a nearest Euclidean distance, acquiring 64 clusters after categorization, and calculating an optimum centroid of an acquired cluster. The present invention can rapidly and precisely select a globally optimal K-means cluster centroid without requiring iteration, greatly decreasing the effects of Kerr nonlinearity in optical fiber, and enabling an obtained bit error rate performance to be half an order of magnitude higher than previous un-processed performance.

Description

technical field [0001] The invention relates to coherent optical data communication, in particular to an optical fiber nonlinear equalization method used in a coherent optical communication system. Background technique [0002] In order to meet the increasing demand of network traffic, high-order modulation signals with high spectral efficiency, such as M-ary phase-shift keying (M-PSK) and M-ary quadrature amplitude modulation (M-QAM), have been widely used to Increase transmission capacity. These signals are highly susceptible to various noises in the transmission system and usually require a high signal-to-noise ratio to obtain the desired bit error rate (BER). Furthermore, a high power signal is essential for an adequate signal-to-noise ratio. However, once these high-power signals are launched into the fiber, the nonlinear damage caused by the Kerr effect will be unavoidable. Therefore, the Kerr nonlinearity in optical fibers limits the performance of high-order modul...

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Problems solved by technology

Therefore, a large training sample will always lead to a longer implementation time of SVM

In addition, ANN also has similar problems with SVM: the learning time is too long, and sometimes the purpose of learning cannot be achieved

As the first step of the K-means clustering method, it is also a very important step. If the selection of the centroid is not the exact position in the data set, the results obtained by the clustering method will often appear due to the randomness of the initial centroid selection. Situations where the clustering results are unsatisfactory

[0006] 2. As the input centroid k increases, that is, the number of clusters to be classified increases, the k-means clustering algorithm can easily fall into a local optimal dilemma, because the criterion function in the k-means algorithm is a non-convex square error evaluation function, which pushes the algorithm away from the search range of the global optimal solution

However, the k-means clustering algorithm is an iterative algorithm. In each iteration, the k-means algorithm needs to calculate the distance between each data point and all center points, and finally ends when the standard measure function begins to converge. Undoubtedly consumes a lot of computing resources and time, especially for massive communication data flow is very fatal

[0008] To sum up, the current traditional K-means clustering method uses the method of randomly selecting the initial centroids for clustering. In the clustering process, repeated iterations and final convergence consume a lot of time. As the number of classification clusters increases , K-means clustering algorithm is difficult to get the global optimal centroid

These problems make the K-means clustering method difficult to be applied in the data processing of the communication system

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