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A Machine Learning-Based Construction Method of Mobile Network Edge Collaborative Caching Model

A technology of mobile network and machine learning, applied in the field of mobile communication, to achieve the effect of improving cache performance and reducing access delay

Active Publication Date: 2022-01-28
派欧云计算(上海)有限公司
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to provide a solution for edge cooperative caching in mobile networks. Based on the computing and storage capabilities of base stations at each level, a caching framework for layered cooperation and same-layer cooperation is formed to support large-scale and various types of intelligent terminals, and to learn Mechanisms to adapt to complex, dynamic and changeable user requests and network environments, and solve the content request routing and cache replacement problems of base stations at all levels, so as to improve users' online experience and reduce user access delays

Method used

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  • A Machine Learning-Based Construction Method of Mobile Network Edge Collaborative Caching Model
  • A Machine Learning-Based Construction Method of Mobile Network Edge Collaborative Caching Model
  • A Machine Learning-Based Construction Method of Mobile Network Edge Collaborative Caching Model

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Embodiment Construction

[0032] The system flow chart of the present invention is as Figure 1~2 shown.

[0033] A method for constructing a communication network model based on mobile edge assisted caching, comprising the following steps:

[0034] Step 1. Establish a network training model based on the content request data sent by the user; that is, the user uses a mobile phone to send a content request (taking video content as an example), and the local usage detection technology (such as field interception, DPI detection) connected to the device is extracted and compared with the video content. Content-related information; the present invention is based on the ZIPF law to model content popularity to user request processing:

[0035]

[0036] where R f Represents the descending rank of content f popularity, and β represents the skewness factor. we assume that

[0037] Step 2, the routing module in the network training model performs base station identification on the content request data sent...

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Abstract

The invention discloses a method for constructing a mobile network edge cooperative cache model based on machine learning. Step 1, establish a network training model according to content request data sent by users; The user sends content request data for base station identification; step 3, the update module in the network training model caches and replaces the local base station according to the replacement action requirements; step 4, repeat steps 1 to 3 until the network training model is stable, this method can significantly reduce user access Latency and improved cache hit rate have eased the traffic pressure on the backbone network.

Description

technical field [0001] The invention relates to the field of mobile communication, in particular to a machine learning-based mobile network edge cooperative caching mechanism. Background technique [0002] With the rapid development of wireless access technology and mobile devices, Internet services and applications are gradually migrating to mobile networks. Mobile network traffic is experiencing explosive growth. Limited wireless spectrum resources and traditional mobile network architecture are difficult to cope with the explosive growth of mobile traffic. At the same time, mobile users' requirements for business service quality are constantly increasing. The study found that most of the traffic is used to download the same popular content, which brings great pressure and challenges to the mobile network. Repeated content transmission increases the pressure on the backhaul link, wastes network resources, and reduces user experience. Cache technology is widely used, from...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04W24/06
CPCH04W24/06H04L41/145
Inventor 王晓飞李丁李文凯韩溢文王晨阳
Owner 派欧云计算(上海)有限公司
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