GTI发布5G无线网络智能化技术需求白皮书-英-56页_1mb

报告摘要

Overview of G T I 5G Radio Network Intelligence White Paper

This document outlines the technical requirements for implementing intelligence in 5G radio networks, focusing on challenges, architectures, and use cases. It emphasizes the need for automation and AI-driven solutions to address growing network complexity and diverse service demands.

Key Challenges in Modern Wireless Networks

Traffic and Energy Growth: Operators face increasing energy costs and environmental pressures, including carbon reduction goals and energy consumption optimization.
Diverse Service Demands: New services like XR, IoT, and autonomous driving require deterministic experiences, precise service recognition, and adaptive network management.
Network Complexity: Multiple RATs, frequencies, and heterogeneous network structures demand intelligent monitoring and real-time decision-making.
Operational Pressures: Manual interventions are insufficient for large-scale networks, necessitating automation to improve efficiency and reduce OPEX.

Vision for 5G-AN (Autonomous Network)

G T I promotes a 5G-AN framework with six levels of autonomy (L0 to L5), aiming for full automation (L4) by 2025.
Key objectives include unlocking new services, reducing human dependency, and achieving "zero-fault" networks through predictive capabilities.
Cross-industry collaboration is encouraged, supported by standards like 3GPP, TM Forum, and ETSI.

RAN Intelligence Technical Requirements

Architecture: Vertical layers for cross-domain, network, and NE intelligence ensure layered processing.
- Data Perception and Decision-Making: Requires intelligent data collection from UEs, devices, and network elements for optimized resource allocation.
- Large Models and AI: Utilizes generative AI, machine learning (ML), and deep learning for enhanced performance in areas like channel characterization, beam management, and fault prediction.
- Digital Twin and Intent-Based Management: Supports simulation, prediction, and intent translation for flexible network adaptations.
Specific Requirements:
- Precision in Service: AI-driven service recognition for accurate QoE guarantees.
- Environmental and Spectral Efficiency: Real-time environmental sensing and spectral grid modeling to enhance user experiences.
- Energy Efficiency: Balancing power consumption with network performance.
Security and Reliability: Incorporates monitoring for anomalies to prevent failures and ensure network stability.

Use Cases and Benefits

Intelligent Multi-Band Coordination: Enhances connectivity through virtual grid predictions, reducing handover latency and improving spectral efficiency.
Designated Application Experience Guarantee: Ensures high QoE through fine-grained service recognition and adaptive optimization.
Intelligent User Orchestration: Flexibly adapts network services based on user needs, supporting diverse applications and scenarios.
Energy Saving: Achieves up to 10% energy reduction while maintaining network performance.
Fault Prediction and Prevention: Enables proactive issue resolution, reducing service disruptions.
Overall Gain: Simplifies operations, improves reliability, and paves the way for smarter networks with tangible benefits like reduced OPEX and enhanced user satisfaction.

Potential Technologies

Large Language Models (LLMs) and AI: Used for natural language processing, intent translation, and distributed cognition across network domains.
Digital Transformation: Leverages open APIs and standardized frameworks for cross-vendor collaboration.

Data and Standardization Requirements

Data Collection: Multi-dimensional data from NEs, UEs, and external sources is essential for intelligent processing.
Standardization: 3GPP and industry consortia are finalizing standards for AI-driven features, ensuring interoperability and scalability.

In conclusion, the white paper emphasizes that RAN intelligence is crucial for meeting future network demands, with AI and automation as key enablers for sustainable and efficient 5G deployments.

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