6G AI-Native Network Infrastructure
A Real-time Data Collection Approach for 6G AI-native Networks
This paper proposes a novel real-time data collection method for 6G AI-native networks. It integrates data acquisition in parallel with bitstream processing, leveraging data probes in software-defined wireless communication. A data support system unifies heterogeneous data for AI model training and decision-making. The approach is validated on a Kubernetes-based 6G testbed using OpenAirInterface5G and Open5GS, demonstrating significant improvements in efficiency and reduced latency compared to traditional methods like Wireshark, particularly for network traffic prediction.
Executive Impact & Key Metrics
For enterprises transitioning to 6G, this research provides a blueprint for an AI-native network foundation that dramatically enhances operational intelligence and automation. By enabling real-time data collection without performance overhead, it allows for proactive network management, predictive maintenance, and optimized resource allocation. This leads to reduced operational costs, improved service reliability, and accelerated AI model deployment for critical network functions, offering a competitive edge in rapidly evolving digital infrastructures.
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CPU Resource Reduction
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Memory Resource Reduction
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Data Collection Latency Reduction
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AI Model Accuracy (Traffic Prediction)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Problem Statement & Motivation
Proposed Parallel Architecture
Key Innovations & Advantages
System Implementation & Testbed
Performance Metrics
Future Directions
Challenges in 6G AI-Native Data Collection
Traditional 6G data collection methods struggle with real-time acquisition and processing, often relying on external tools like Deep Packet Inspection (DPI) which consume significant resources and introduce latency. This asynchronous operation hinders the full potential of AI-native networks that require continuous, structured data feeds for autonomous learning and decision-making embedded at their core.
Real-time Data Collection Workflow
Bitstream Ingress
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Header Parsing (Main Thread)
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Payload Processing (Main Thread)
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Data Probes (Parallel Thread)
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Real-time Data Capture
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Store as JSON/Prometheus
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Processed Bitstream Egress
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Kubernetes-based 6G Testbed Validation
A 6G communication testbed was built on Kubernetes, utilizing OpenAirInterface5G (OAI) and Open5GS for core and access network functions. This setup allowed for containerized deployment of Network Entities (NEs), enabling granular monitoring of each processing step. The data support system, based on Prometheus, aggregates multi-level data and provides a data service interface for AI models. A network traffic prediction case study, using a pre-trained TabNet regression model, successfully demonstrated the system's ability to provide real-time network and system status data for intelligent decision-making, confirming its operational viability and benefits.
Real-time Performance Gains
32.7% Reduction in CPU Resource ConsumptionAdvancing AI-Native Networks
Future research will focus on further enhancing data collection and validation systems, improving system scalability and elasticity, and enabling more comprehensive all-round learning. This includes exploring advanced AI algorithms that can dynamically adapt data collection strategies based on real-time network conditions and AI model requirements, further solidifying the foundation for truly autonomous 6G networks.
Calculate Your Enterprise's Potential Savings
Understand the tangible financial and operational benefits of implementing AI-native network data solutions tailored to your organization.
Estimated Annual Savings
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Total Hours Reclaimed Annually
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Implementation Roadmap
Our structured approach ensures a smooth transition to an AI-native 6G network, minimizing disruption and maximizing ROI.
Phase 1: Discovery & Assessment (2-4 Weeks)
Comprehensive analysis of existing network infrastructure, data sources, and AI integration readiness. Define key performance indicators (KPIs) and operational objectives.
Phase 2: Architecture Design & Testbed Deployment (6-10 Weeks)
Design of the parallel data collection architecture, integration planning for data probes, and deployment of a Kubernetes-based 6G testbed (OpenAirInterface5G, Open5GS) in a sandboxed environment.
Phase 3: Data Pipeline & AI Model Integration (8-12 Weeks)
Implementation of the real-time data support system (Prometheus), configuration of data exposure interfaces, and integration of initial AI models (e.g., TabNet for traffic prediction) with the collected data streams.
Phase 4: Validation, Optimization & Scale (Ongoing)
Thorough testing and validation of the integrated system, performance tuning, and iterative refinement of AI models. Phased rollout to production environment with continuous monitoring and optimization.
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