Role-Based AI Agents for Scalable 5G Network Management
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Solution Overview
Problem
The complexity and manual nature of managing 5G networks lead to inefficiencies, increased risk of human error, and limited scalability, hindering the potential of advanced AI techniques in network data analysis and human feedback integration.
Innovation Solution
A system and method utilizing role-based agents and foundation models to automate network management, enabling decentralized learning and decision-making, with agents trained to handle queries, collect relevant data, and formulate answers, leveraging AI for dynamic adaptation, resource optimization, and personalized services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual network management is used, then human expertise and judgment can be applied, but the system becomes time-consuming and prone to human error
Solution Approach 1:
The system implements self-service through autonomous AI agents that automatically perform network management tasks including monitoring, troubleshooting, and optimization without human intervention. The agents independently analyze network data, diagnose issues, and execute corrective actions, eliminating the need for manual intervention while maintaining high reliability through continuous automated operation.
Solution Approach 2:
The patent replaces manual mechanical network management processes with intelligent software agents equipped with machine learning capabilities. These agents substitute human operators by automatically performing complex tasks such as network slicing management, resource allocation, and fault diagnosis, thereby reducing both time consumption and human error while maintaining expertise through AI algorithms.
2Adaptability or versatility
If manual network management is used, then current processes can be maintained, but scalability becomes increasingly unsustainable
Solution Approach 1:
The system segments network management into multiple specialized AI agents, each responsible for specific functions such as monitoring, troubleshooting, optimization, and resource allocation. This segmentation enables parallel processing of management tasks across different network domains, allowing the system to scale efficiently as network complexity increases without proportionally increasing manual workload.
Solution Approach 2:
The AI agent framework implements universality by designing multi-functional agents that can perform various network management tasks across different network types and scenarios. The agents are equipped with generalizable machine learning models that adapt to diverse network configurations, enabling a single agent architecture to handle everything from 4G to 5G networks, thereby improving scalability without requiring separate manual processes for each scenario.
3Extent of automation
If AI techniques are applied to network data analysis, then analysis capability is enhanced, but the system lacks effective leverage of advanced AI potential
Solution Approach 1:
The system implements dynamic AI automation where agents continuously adapt their behavior based on real-time network conditions and learned patterns. The machine learning models are trained on historical network data and dynamically adjust their analysis and decision-making capabilities, enabling the system to leverage advanced AI potential while managing complexity through adaptive, context-aware automation that evolves with network requirements.
Solution Approach 2:
The patent introduces AI agents as intermediaries between raw network data and management decisions. These agents serve as intelligent mediators that process complex network data, apply machine learning algorithms, and generate actionable insights, thereby enhancing analysis capability while encapsulating AI complexity within the agent layer. This intermediary approach allows advanced AI techniques to be leveraged without exposing underlying complexity to end users or requiring complex integration across the entire system.
Data Source
AI summary
Aspects of the subject disclosure may include, for example, a device, including: a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations of: training a role-based agent to handle queries from users of a network; training a role-based foundation model to retrieve data relevant to a role of a user; receiving a query from the user; and providing the query to the role-based agent, wherein the role-based agent uses an associated role-based foundation model to process the query, collect relevant data from the network, and formulate an answer to the query. Other embodiments are disclosed.