Satellite network flow prediction method based on space-time correlation

Abstract

The invention discloses a satellite network flow prediction method based on space-time correlation. The method comprises the following steps: extracting satellite space-time correlation flow; reducingrelated flow dimensions of singular matrix decomposition, and extracting features; and establishing a satellite network traffic prediction model based on the gradient boosting regression tree. According to the method, singular matrix decomposition is carried out on the collected space-time flow to obtain the space-time related flow after dimension reduction, the space-time related flow serves asprediction input of a gradient boosting regression tree, then training and testing are carried out, and finally an accurate prediction value is output. According to the method, a new model is constructed by the gradient boosting regression tree in the gradient descending direction, the algorithm convergence method is optimized by improving the learning rate, in addition, the model is continuouslyupdated by minimizing the expected value of the loss function, so that the model tends to be stable, and finally, a future value is predicted by using test data for verification. Decision support is provided for planning of satellite network flow, and the method has a good application prospect.

Description

technical field [0001] The invention relates to a prediction algorithm of satellite network traffic, in particular to a satellite network traffic prediction method based on time-space correlation. Background technique [0002] The space-space-ground integrated network connects users, aircraft and various communication platforms on the ground, at sea, in the air and in deep space through inter-satellite links and satellite-ground links to achieve large-capacity information acquisition, rapid processing and efficient transmission. information network. As the backbone part of it, the satellite network has the incomparable advantages of traditional ground networks such as global coverage, simple access, support for multiple services, and on-demand bandwidth allocation. play an increasingly important role. [0003] Traffic planning is a method for scientifically allocating traffic in communication network design. An optimized traffic allocation method can improve network utiliz...

AI Technical Summary

Problems solved by technology

However, compared with the ground, the satellite network has limited available resources and time-varying topology. If the traditional ground network traffic prediction algorithm is directly applied to the satellite network, it will inevitably face the challenges of prediction accuracy and operational efficiency.

The combination model based on neural network has achieved good results in prediction due to its strong fault tolerance, fast parallel computing and powerful learning ability, but it can only rely on experience in parameter selection, and the algorithm has high time complexity, Long training time and slow convergence

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