RAN Digital Twin Using a Vector Database for Real-Time Monitoring
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Solution Overview
Problem
Existing 5G cloud-native networks face challenges in real-time characterization and management due to complexity and volume of data, leading to inefficiencies, human errors, and delayed decision-making, which impacts network performance and reliability.
Innovation Solution
A real-time digital twin representation of network behavior using matrices and real-time computation of Key Performance Indicators (KPIs) is implemented, organized into a vector database, enabling automated network analysis and operational decision-making through workflow automation and similarity search queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual collection and analysis of network performance data is used, then human operators can interpret network behavior, but it results in human errors, inefficiencies, and delays in decision-making
Solution Approach 1:
The patent creates a digital twin (virtual copy) of the radio access network that replicates network behavior and performance data. This digital replica enables automated analysis without human intervention, eliminating human errors while maintaining comprehensive monitoring capabilities, thus improving both reliability and productivity in decision-making
Solution Approach 2:
The system enables self-service through automated KPI computation and analysis algorithms that independently process network performance data without human operators. The digital twin autonomously identifies issues and generates insights, eliminating manual collection and analysis, thereby removing human errors and accelerating decision-making speed
2Loss of information
If traditional database structures are used to store KPIs, then data can be stored systematically, but real-time similarity search and network behavior depiction are difficult to achieve
Solution Approach 1:
The patent transforms traditional tabular KPI data into vector representations, changing the data structure from discrete rows and columns to continuous vector spaces. This parameter change enables real-time similarity search through vector operations while preserving all KPI information, achieving real-time visibility without proportionally increasing organizational complexity
Solution Approach 2:
The system adds a vector dimension to traditional KPI storage by representing network performance data as vectors in a multi-dimensional space. This dimensional transformation enables new query capabilities like similarity search and real-time behavior depiction while maintaining the systematic organization of data, resolving the contradiction between information accessibility and organizational complexity
3Productivity
If full automation of network analysis is implemented, then real-time characterization is achieved, but the complexity of organizing and processing terabytes of data increases
Solution Approach 1:
The digital twin serves as a virtual copy that consolidates terabytes of network performance data into a unified vector representation. This copying approach enables full automation of network analysis by providing a single, organized data structure that can be processed in real-time, achieving high productivity without proportionally increasing processing complexity
Solution Approach 2:
The patent creates a composite data structure that combines multiple KPI types (counters, events, alarms) into unified vector representations. This composite organization integrates heterogeneous data sources into a single processing framework, enabling automated real-time analysis while managing complexity through unified data handling
Data Source
AI summary
A system and method for managing a Radio Access Network (RAN) is disclosed. The method involves receiving performance indicators from network elements and organizing them as structured Key Performance Indicators (KPIs). The structured KPIs are then arranged in a vector database. By querying the vector database, the network behavior of the RAN can be depicted in near real-time. This method provides an efficient and effective approach to monitor and analyze the performance of a RAN, enabling network operators to make informed decisions and optimize network performance.