Non-RT RAN Intelligent Controller Functional Split Architecture
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Solution Overview
Problem
The challenge of facilitating AI/ML lifecycle management in a non-real-time RIC is exacerbated by its ML-specific nature, making it difficult to adapt to a wide range of future applications without a functional split.
Innovation Solution
A method enabling a functional split between a non-real-time RIC and an external AI/ML server through a service management and orchestration entity, involving data collection, transfer, training model input, and configuration phases, with the aid of a transceiver and processor to facilitate intelligent beam management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the non-RT RIC maintains its ML-specific nature without functional split, then it can manage AI/ML lifecycle effectively, but it becomes difficult to adapt to a wide range of future applications
Solution Approach 1:
The patent segments the non-RT RIC into two distinct functional components: a domain-generic SMO entity that handles resource management and orchestration, and a domain-specific non-RT RIC that focuses on AI/ML lifecycle management. This segmentation allows the system to maintain ML-specific capabilities while gaining adaptability to diverse applications through the generic SMO layer.
Solution Approach 2:
The SMO entity acts as an intermediary between the external environment and the ML-specific non-RT RIC. It provides a standardized interface that enables different applications and services to interact with the AI/ML lifecycle management without requiring changes to the core ML-specific functionality, thus enhancing adaptability while preserving specialized capabilities.
2Ease of operation
If the non-RT RIC is made ML-specific for effective AI/ML lifecycle management, then AI/ML operations are optimized, but flexibility for diverse applications is reduced
Solution Approach 1:
The patent extracts the domain-specific AI/ML lifecycle management functionality into a separate non-RT RIC component, while the SMO entity handles domain-generic operations. This extraction allows the ML-specific component to be optimized for AI/ML operations without being constrained by diverse application requirements, while the SMO provides the necessary flexibility through standardized interfaces.
Solution Approach 2:
The SMO entity is designed with universal, domain-generic functions that can serve multiple different applications and services. By providing a standardized interface and common resource management capabilities, it enables the ML-specific non-RT RIC to maintain ease of operation for AI/ML lifecycle management while the SMO handles the adaptability to diverse services.
Data Source
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AI summary
The present disclosure relates to a 5G communication system or a 6G communication system for supporting higher data rates beyond a 4G communication system such as long term evolution (LTE). A service management and orchestration (SMO) entity enabling a functional split between a non-real-time (RT) radio access network (RAN) intelligent controller (RIC) and an external artificial intelligence (AI)/machine learning (ML) server will, during a data collection phase, utilize the SMO entity and the non-RT RIC to collect and process RAN data and non-RAN data and, during a data transfer phase, transfer processed RAN and non-RAN data from the SMO entity to an external AI/ML server via an interface. During a training model input phase, the SMO entity receives a trained AI/ML model, metadata, and training results from the external AI/ML server via an interface and, during a configuration phase, the SMO entity uses the trained AI/ML model within the SMO entity and the non-RT RIC to transfer configuration parameters to a near-RT RIC.