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5G base station cluster KPI prediction method and system based on multi-reservoir fuzzy cognitive map

A fuzzy cognitive map and prediction method technology, applied in the field of communication, can solve problems such as long training time, cover model interpretability, etc., and achieve high prediction accuracy and high-precision interpretable prediction effect

Pending Publication Date: 2022-02-18
SHANDONG NORMAL UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] Now commonly used time series data prediction models include DNN, LSTM, GRU and their variants, etc. These models have more parameters, and the training time is relatively long, and their black box characteristics cover up the interpretability of the model.

Method used

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  • 5G base station cluster KPI prediction method and system based on multi-reservoir fuzzy cognitive map
  • 5G base station cluster KPI prediction method and system based on multi-reservoir fuzzy cognitive map
  • 5G base station cluster KPI prediction method and system based on multi-reservoir fuzzy cognitive map

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Experimental program
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Effect test

Embodiment 1

[0033] Terminology:

[0034] KPI: (Key Performance Indicator, Key Performance Indicator), the base station network cluster generates a large amount of data, such as user experience, connection density, end-to-end delay, mobility, traffic density, etc., these data KPI of the base station network.

[0035] FCM: Fuzzy cognition, is a weighted award drawing of n concept nodes consisting of a concept node, a status value, and a relationship. It combines fuzzy logic and neural networks, which is the power of system status prediction and interpretation knowledge. Model, in a fuzzy cognitive diagram with n nodes, each node represents a concept in the system, which can be an event, target, and trend, etc. of the system, and each concept passes its properties through a status value. The causality between concepts affects the relationship with an arc as an arc.

[0036] ESN: Echo Status Network, is one of the library computing system, simple training, good predictive ability to nonlinear or ...

Embodiment 2

[0104] This embodiment provides a 5G base station cluster of 5G base station cluster based on a multi-storage layer, including:

[0105] The data acquisition module is configured to get the KPI original sequence data acquired in the 5G base station cluster;

[0106] The data pre-processing module is configured to prepare the KPI raw sequence data to obtain the KPI dynamic time series of the base station network;

[0107] The status feature extraction module is configured to: based on the fuzzy cognitive graph model, the dynamic time series corresponds to the concept node in the fuzzy cognitive chart;

[0108] On the basis of the original reasoning structure with fuzzy feedback, fuzzy feedback is added, replacing each concept node of the concept node in the fuzzy cognitive chart to the recovery state network to obtain a multi-storage layer blur cognitive graph model, apply multi-library learning Get the status characteristics of the KPI of each base station;

[0109] The KPI predic...

Embodiment 3

[0111] This embodiment provides a computer readable storage medium, which stores a computer program that implements the steps in the carrier network traffic prediction method based on the gram-based corner field as described above when executed by the processor.

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Abstract

The invention belongs to the technical field of communication, and provides a 5G base station cluster KPI prediction method and system based on a multi-reservoir fuzzy cognitive map, and the method comprises the following steps: obtaining KPI original sequence data of all base stations in a 5G base station cluster; preprocessing the KPI original sequence data to obtain a KPI dynamic time sequence of the base station network; enabling the dynamic time sequence to correspond to concept nodes in a fuzzy cognitive map based on a fuzzy cognitive map model; on the basis of an original reasoning structure with fuzzy feedback, adding fuzzy feedback, replacing each concept node in a fuzzy cognitive map with a rising state network to obtain a multi-reservoir fuzzy cognitive map model, and applying multi-library learning to obtain state features of KPI of each base station; and based on the state characteristics of the KPI, obtaining the KPI at the next moment through iterative reasoning of fuzzy causal relationship dynamics.

Description

Technical field [0001] The present invention belongs to the field of communication, and more particularly to a 5G base station cluster KPI prediction method and system based on a multi-storage layer blur cognitive graph. Background technique [0002] The statement of this section is merely the background technology information associated with the present invention, which is not necessarily constituted in prior art. [0003] Compared with 2G, 3G, 4G networks, the frequency of 5G networks is higher, and the loss in the process of communication is large. Therefore, it is necessary to build a more intensive base station, the KPI of the base station network, excavate the law behind these KPIs is important for assistive decision-making. Through the prediction of a certain area base station network KPI, the change in the region on key indicators can be known to a certain extent, and it can assist operators to establish a reasonable emergency policy. [0004] Problems in prior art: [00...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04W24/08G06N3/04
CPCH04W24/08G06N3/043
Inventor 骆超刘灿娜邵锐
Owner SHANDONG NORMAL UNIV
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