5G Service Type Identification via Distributed Big Data Analysis
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Solution Overview
Problem
In 5G communications networks, identifying service types in multi-user and multi-service scenarios is challenging due to the complexity of big data processing, which affects intrusion detection, differentiated services, traffic monitoring, and quality of service guarantees.
Innovation Solution
A communication method utilizing big data analysis technology, where a database network element collects and synchronizes service transmission data and network data, sends this data to a data analytics network element to generate feature index lists, and uses these lists to identify service types, reducing delays through distributed deployment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If big data analysis technology is used to identify service types in 5G networks, then service type identification accuracy is improved, but data processing complexity increases
Solution Approach 1:
The patent segments the big data processing system into multiple specialized network elements: data collection network elements, data storage network elements, data processing network elements, and service type identification network elements. Each element handles specific tasks in the data flow, dividing the complex processing workload into manageable, specialized components that improve identification accuracy without overwhelming a single system.
2Loss of time
If distributed deployment is used to reduce delays, then response time is improved, but system complexity increases
Solution Approach 1:
The patent transitions from centralized to distributed deployment across multiple network elements, adding spatial dimensionality to the system architecture. Data collection, storage, processing, and identification functions are distributed across geographically separated network elements, reducing processing delays through parallel operations while managing complexity through standardized inter-element communication protocols.
3Loss of information
If comprehensive data collection from multiple sources is performed, then identification completeness is improved, but data management complexity increases
Solution Approach 1:
The patent introduces specialized intermediary network elements that mediate between diverse data sources and the analysis system. Data collection network elements aggregate data from multiple sources (user devices, network infrastructure, application servers), while data storage and processing elements standardize and manage this heterogeneous data, ensuring complete service type information is captured without overwhelming the management system.
Data Source
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AI summary
This application provides a communication method, a database network element, a data analytics network element, a control plane network element, and a user plane network element. The communication method includes: obtaining, by the database network element, training data, where the training data includes service transmission data and network data; sending, by the database network element, the training data to the data analytics network element; and receiving, by the database network element, first information from the data analytics network element, where the first information includes a first feature index list set, a service identifier corresponding to each feature index list in the first feature index list set, and a DNN corresponding to the service identifier. The communication method, the database network element, the data analytics network element, the control plane network element, and the user plane network element that are provided in this application can identify a service type in a communications network supporting big data.