LLM Agent for Context-Aware RAN Management
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Solution Overview
Problem
Current methods for managing Radio Access Networks (RAN) are manual, error-prone, and require extensive technical expertise, limiting flexibility and efficiency in monitoring, querying, and controlling network performance.
Innovation Solution
The implementation of an AI-assisted management system using a large language model (LLM) that transforms technical queries into general domain queries, generates executable code, and interacts with RAN databases to provide context-aware management capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual monitoring and querying methods are used in RAN management, then system reliability is maintained through human expertise, but operational efficiency and ease of use deteriorate due to tedious and error-prone processes
Solution Approach 1:
The patent introduces an intermediary layer between the user and the RAN management system. This intermediary automatically parses natural language queries, translates them into appropriate query languages (SQL, YAML, etc.), and executes them against the RAN database. This eliminates the need for users to manually write complex queries while maintaining system reliability through automated validation and error handling.
Solution Approach 2:
The system enables self-service by allowing users to interact with RAN management through natural language without requiring specialized knowledge. The automated query generation and execution capabilities allow users to independently perform monitoring and analysis tasks that previously required expert intervention, thereby improving both productivity and ease of operation.
2Ease of operation
If natural language processing is implemented to enable non-technical users to query the RAN, then ease of operation improves, but system complexity increases due to the need for advanced machine learning models
Solution Approach 1:
The patent segments the NLP system into multiple specialized components: a natural language parser that processes user input, a query translator that converts parsed input into executable query languages, and an execution engine that runs queries against the RAN database. This modular architecture reduces overall system complexity by allowing each component to be developed and maintained independently with well-defined interfaces.
Solution Approach 2:
The system implements a universal query processing framework that can handle multiple query types (monitoring, analysis, troubleshooting) and multiple query languages (SQL, YAML, etc.) through a single NLP interface. This multi-functional approach reduces complexity compared to having separate specialized systems for each query type, as the core NLP and translation components remain shared across all functionalities.
3Reliability
If vendor-defined dashboards are used for monitoring, then system stability and reliability are maintained, but adaptability and flexibility deteriorate as only predefined KPIs can be observed
Solution Approach 1:
The patent implements a dynamic query system that adapts to user needs in real-time. Instead of being limited to static, pre-defined dashboards, the system can dynamically generate and execute custom queries based on natural language input. This allows the monitoring capabilities to be both stable (through automated validation) and flexible (through customizable query generation), resolving the contradiction between reliability and adaptability.
Data Source
AI summary
This disclosure relates to methods, systems, and devices for AI/ML assisted management of a radio access network (RAN). In particular, an LLM is employed to generate answer to user queries, for example in the form of code that can be executed upon streaming data from the RAN, including performance management and control management databases. To provide prompts the LLM can understand, an LLM agent employs a knowledge base and experience base to transform the language of the queries from the highly technical telecommunications domain to a general domain. Frequently asked questions and their answers may be stored in the experience base and used instead of using the LLM.