Joint prediction method of base station traffic

A prediction method and base station technology, applied in digital transmission systems, electrical components, transmission systems, etc., can solve problems such as low accuracy of predicted traffic data, and achieve the effects of easy prediction, high prediction accuracy, and good regularity

Inactive Publication Date: 2019-04-16
NANJING UNIV OF POSTS & TELECOMM
View PDF4 Cites 10 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to propose a joint prediction method of base station traffic, which solves the problem of low accuracy when the prior art is used to predict traffic data,

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Joint prediction method of base station traffic
  • Joint prediction method of base station traffic
  • Joint prediction method of base station traffic

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0029] Neural networks have better performance in nonlinear traffic data. A type of recurrent neural network (RNN) called an echo state network (ESN) does a good job of predicting traffic trends. But when traffic trends become more complex, there is also some error between actual and predicted values. Therefore, first choose the wavelet transform (WT) algorithm to decompose the initial data into different coefficients. Then, use the different characteristics of the coefficients to perform a single reconstruction, and then use ESN and ARIMA to predict the flow data of signals of different frequencies, and linearly accumulate the predicted values ​​​​of a single sequence to complete the final flow prediction. Finally, according to the error value, the joint prediction method has better accuracy than the single model.

[0030] The invention will be described in further detail below in conjunction with the accompanying drawings.

[0031] Such as figure 1 As shown, the present ...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention provides a joint prediction method of base station traffic. The problem that the traditional linear algorithm is bad in prediction performance when the traffic data is nonlinear and hasa sudden change value is solved. The method comprises the following steps: firstly collecting traffic data from a base station as a data set, performing data preprocessing on an abnormal value and a missing value; decomposing processed data by adopting wavelet transform, enabling the traffic data to be smooth and easy to predict; performing single reconstruction on a sequence obtained through decomposition, wherein a low-frequency signal is predicted by adopting an echo state network model, and a high-frequency signal performs prediction by adopting an autoregression integral sliding average model; and finally performing linear accumulation on the prediction numerical value of the single sequence to obtain a final result. Compared with the single model prediction, the joint model method disclosed by the invention can reach better prediction, the reduced average absolute percentage error can achieve 6%, and the normalization root mean square error is reduced to a certain degree; the traffic data prediction accuracy of the base station is improved, and the network resource reasonable allocation can be improved.

Description

technical field [0001] The invention relates to the technical field of predicting base station flow by using ESN and ARIMA models, in particular to a base station flow prediction method based on wavelet decomposition transformation combined with echo network state. Background technique [0002] With the development of wireless communication technology, traffic forecasting has become one of the hottest research topics in recent years. Large-scale multimedia services are considered to be one of the most prominent features of smart cities. On the one hand, most users prefer to use mobile devices to surf the Internet, which will consume a lot of traffic data from wireless transmission. On the other hand, communication operators also need to know the amount of traffic data used in certain areas, and master the regional traffic data. Assignment levels. When the traffic peak exceeds the threshold that the equipment can withstand, the equipment in the base station should be replac...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Applications(China)
IPC IPC(8): H04W24/06H04L12/24
CPCH04L41/145H04L41/147H04W24/06
Inventor 周亮唐菁魏昕包秋霞高赟
Owner NANJING UNIV OF POSTS & TELECOMM
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products