ORAN E2 Node KPI Reporting With Adaptive Reporting Periods

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Solution Overview

Problem

E2 nodes in open radio access networks (ORAN) do not have a mechanism to dynamically adjust reporting frequencies based on network traffic fluctuations, leading to potential subscription rejections and performance degradation due to inefficient resource utilization.

Innovation Solution

Implement a machine learning-based model to predict and recommend optimal reporting periods for E2 nodes, taking into account resource utilization, historical data, and priority levels to ensure efficient resource allocation and maintain network performance.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If E2 nodes report data frequently to maintain network performance, then data reporting reliability is improved, but network resource utilization deteriorates and subscription rejections occur

Engineering Contradiction:

Improvedata reporting reliabilityVSAvoidnetwork resource utilization

Core Design Contradiction:

ReliabilityVSQuantity of substance

Solution Approach 1:

The system dynamically adjusts reporting periods based on real-time network conditions and node capabilities. The controller modifies reporting frequencies adaptively, transitioning from static to dynamic reporting schedules that balance reliability requirements with resource availability, preventing both over-reporting and under-reporting scenarios

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system changes the reporting period parameter based on node resource utilization levels and network traffic conditions. By adjusting this critical parameter dynamically, the system optimizes the balance between maintaining reliable data reporting and conserving network resources, allowing flexible adaptation to varying operational conditions

Inventive Principle:

Principle #35Parameter changes

2Reliability

If E2 nodes increase reporting frequency to maintain network performance, then quality of service is improved, but energy consumption increases

Engineering Contradiction:

Improvequality of serviceVSAvoidenergy consumption

Core Design Contradiction:

ReliabilityVSUse of energy by moving object

Solution Approach 1:

The system implements periodic reporting with variable periods rather than continuous or fixed-frequency reporting. By optimizing the reporting period length based on network conditions and node capabilities, the system maintains quality of service while minimizing unnecessary energy consumption during periods when frequent reporting is not critical

Inventive Principle:

Principle #19Periodic action

Solution Approach 2:

The system enables E2 nodes to self-regulate their reporting behavior based on their own resource utilization states and network conditions. Nodes autonomously adjust their reporting frequencies without requiring constant external control, reducing energy consumption while maintaining service quality through self-optimized operation

Inventive Principle:

Principle #25Self-service

3Quantity of substance

If manual adjustments are made to optimize reporting periods, then resource utilization is improved, but operational complexity increases and trial and errors are required

Engineering Contradiction:

Improveresource utilizationVSAvoidoperational complexity

Core Design Contradiction:

Quantity of substanceVSEase of operation

Solution Approach 1:

The system implements automated feedback loops where the controller continuously monitors node resource utilization, network traffic patterns, and reporting effectiveness. This feedback mechanism enables automatic optimization of reporting periods without manual intervention, eliminating trial-and-error operations and reducing operational complexity through data-driven decision making

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system replaces manual mechanical adjustment processes with automated computational optimization. Instead of requiring operators to manually tune reporting parameters through iterative trial and error, the system uses algorithms and machine learning models to automatically determine optimal reporting periods, substituting human operational complexity with automated intelligent control

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS12615195B2Dynamic node key performance indicator reporting in open radio access network

Publication Date: 2026.04.28 DELL PROD LP

AI summary

Dynamic node key performance indicator reporting in an open radio access network (e.g., using a computerized tool), is enabled. For example, a system can comprise a processor and a memory that stores executable instructions that, when executed by the processor, facilitate performance of operations, comprising determining resource utilization of a network node, determining a requested reporting period of a key performance indicator, applicable to the network node, requested by an extended application of a radio access network intelligent controller, and using a reporting model generated using machine learning based on past resource utilization, other than the resource utilization, and past reporting periods, other than the requested reporting period, generating a reporting period recommendation applicable to the network node and the extended application.