Intent-Based O-RAN Resource Optimization via SMO Translation
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Solution Overview
Problem
Current Open Radio Access Network (O-RAN) frameworks lack procedures for optimizing RAN or O-Cloud resources based on user-defined intents, limiting multivendor operability and requiring technical expertise for policy configuration.
Innovation Solution
The system enables users to input intent policies through an intent interface termination, which determines and translates these policies into SMO configurations implementable on near-real-time RICs and O-Cloud resources, allowing for abstraction level translation from business to system-level commands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional vendor-specific RAN configurations are used, then technical control and precision are maintained, but multivendor operability and ease of operation deteriorate
Solution Approach 1:
The patent introduces an intermediary translation layer that converts high-level intent policies into vendor-specific technical configurations. This mediator handles the abstraction between user-friendly intent expressions and complex technical implementations, enabling multivendor operability without requiring users to understand vendor-specific technical details.
Solution Approach 2:
The patent segments the policy configuration process into two distinct layers: an intent policy layer for user input and a technical configuration layer for implementation. This segmentation allows users to operate at a high level while technical details are handled separately by the translation mechanism, improving ease of operation across multiple vendors.
2Manufacturing precision
If detailed technical policies are required for RAN optimization, then manufacturing precision and control are improved, but device complexity and ease of operation worsen
Solution Approach 1:
The translation layer acts as an intermediary that automatically generates precise technical policies from high-level intents. This mediator ensures manufacturing precision is maintained in the implemented configurations while hiding the complexity from users, as the translation process handles the detailed technical mappings automatically.
Solution Approach 2:
The system performs self-service by automatically translating intent policies into technical configurations without requiring user intervention in the complex translation process. The system serves itself by generating the necessary detailed policies from abstract intents, reducing both perceived complexity and actual configuration effort.
3Extent of automation
If automated intent-based optimization is implemented, then extent of automation is improved, but measurement precision and control deteriorate
Solution Approach 1:
The patent incorporates feedback mechanisms where the translation system learns from the effectiveness of generated policies. By monitoring whether intent policies achieve their desired outcomes, the system refines its translation accuracy, maintaining measurement precision while increasing automation. The feedback loop ensures automated decisions remain precise and controllable.
Solution Approach 2:
The automated translation system performs self-service by continuously improving its own translation capabilities through accumulated experience. The system autonomously refines its policy generation without external intervention, maintaining precision through self-learning while maximizing automation extent.
Data Source
AI summary
A method of generating policies/configurations in an open radio access network (O-RAN) service management and orchestration (SMO) framework, the SMO framework including an intent interface termination and a non-real-time RAN intelligent controller (NRT RIC), may include obtaining an input corresponding to an intent policy for at least one operation of the SMO framework, determining the intent policy of the input, generating an SMO policy/configuration based on the intent policy, and implementing the SMO policy/configuration on at least one of a near-real-time RIC (nRT RIC), at least one RAN node, and an O-RAN Cloud (O-Cloud).