O-RAN Resource Optimization via Non-RT RIC Dynamic Adjustment
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Solution Overview
Problem
The current Open Radio Access Network (O-RAN) framework lacks a feasible solution for dynamic optimization of O-cloud resources to meet agreed service level agreements (SLAs) between host and tenant operators for Radio Access Network (RAN) sharing, due to limitations in data analytics and optimization capabilities of the Non-RT RIC rApp, and the absence of policy-based assistance to service management and orchestration functions.
Innovation Solution
A method and apparatus that utilize the R1 interface between the rApp and Non-RT RIC to perform dynamic optimization of O-cloud resources by receiving information on sharing services, transmitting subscription requests, instantiating network functions, and monitoring performance, enhancing O-cloud resource optimization for RAN sharing through the Non-RT RIC installed on host and tenant operator SMO platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the current O-RAN framework is used with Non-RT RIC rApp, then data analytics and RAN resource optimization can be performed, but dynamic optimization of O-cloud resources to meet SLAs cannot be achieved
Solution Approach 1:
The patent implements dynamic optimization by enabling the Non-RT RIC rApp to continuously monitor O-cloud resource utilization and dynamically adjust resource allocation based on real-time conditions and SLA requirements. The system transitions from static resource allocation to dynamic adjustment, allowing the O-cloud infrastructure to adapt its capacity and configuration according to changing network demands and service level agreements between host and tenant operators.
Solution Approach 2:
The patent establishes a feedback mechanism where the Non-RT RIC rApp receives performance data from O-cloud resources, analyzes SLA compliance status, and generates optimization policies that are fed back to the O-cloud infrastructure. This closed-loop feedback system enables continuous improvement of resource allocation efficiency while ensuring SLA adherence, with the rApp adjusting its optimization strategies based on observed performance outcomes.
2Ease of manufacture
If RAN sharing is implemented between host and tenant operators, then cost-effective coverage increase is achieved, but the ability to optimize shared O-cloud resources according to operator-specific SLAs is lost
Solution Approach 1:
The patent applies local quality by enabling each operator (host and tenant) to have customized optimization policies tailored to their specific SLA requirements while sharing the same O-cloud infrastructure. The Non-RT RIC rApp implements operator-specific optimization strategies, allowing each operator to optimize resources according to their unique service level agreements, performance targets, and business requirements, rather than applying a uniform optimization approach to all shared resources.
Solution Approach 2:
The patent segments the optimization control by separating the optimization logic into operator-specific policy modules within the Non-RT RIC rApp. Each operator's SLA requirements and optimization preferences are handled as distinct segments, allowing independent optimization of O-cloud resources for each operator while maintaining efficient shared infrastructure utilization. This segmentation enables multi-operator RAN sharing with customized resource management for each participant.
3Ease of operation
If policy-based assistance to SMO anchored functions is added, then operator-friendly access to intelligence learning is improved, but system complexity increases
Solution Approach 1:
The patent introduces the Non-RT RIC rApp as an intermediary layer between the O-cloud infrastructure and the Service Management and Orchestration (SMO) anchored functions. This intermediary provides policy-based assistance that translates complex AI/ML optimization capabilities into operator-friendly policies, shielding operators from the underlying system complexity while enabling access to intelligence learning. The rApp mediates between the sophisticated optimization algorithms and the operators' simplified policy interfaces.
Solution Approach 2:
The patent enables the SMO anchored functions to self-configure and self-optimize by providing them with policy-based assistance from the Non-RT RIC rApp. The system implements self-service capabilities where the SMO functions can autonomously adjust their operations based on optimization policies generated by the rApp, reducing the need for manual configuration and intervention. This self-service approach simplifies operator interaction while maintaining sophisticated optimization capabilities.
Data Source
AI summary
A method performed by a processor executing a first application includes receiving, information regarding one or more sharing services registered by a second application. The method includes transmitting a subscription request to the one or more sharing services. The method includes receiving, over the interface from the second application in response to the subscription request, a subscription confirmation indicating a successful subscription to the one or more sharing services. The method includes transmitting, over the interface to the second application, a network function instantiation request that requests instantiation of a network function that utilizes the one or more network resources of the second network operator. The method includes receiving, in response to the network function instantiation request, a notification confirming that the network function is instantiated. The method further includes monitoring performance of the instantiated network function on the one or more network resources.