Adaptive RIC KPI Sampling with Quantized Risk Evaluation
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Solution Overview
Problem
The open RAN architecture faces issues with excessive signaling overhead and inefficient resource usage due to high sampling frequencies of key performance indicators (KPIs), compounded by the abundance of available KPIs and data applications, leading to increased energy consumption and hardware resource consumption.
Innovation Solution
A radio intelligent controller (RIC) adaptively adjusts the sampling rate of KPIs based on a quantized risk evaluation, reducing sampling frequency for low variation or risk and increasing it for high variation or risk to optimize signaling overhead and conserve resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high sampling frequencies are used for KPIs, then measurement precision is improved, but signaling overhead increases and energy consumption increases
Solution Approach 1:
The patent applies dynamics by making the sampling rate adjustable rather than fixed. The RIC dynamically changes the sampling rate of KPIs based on quantized risk evaluation results, allowing the system to adapt between high sampling rates (for precision) and low sampling rates (for energy saving) according to actual network conditions and risk levels.
Solution Approach 2:
The patent changes the sampling rate parameter based on quantized risk evaluation. When risk is low, the sampling rate is reduced to conserve energy; when risk is high, the sampling rate is increased to maintain measurement precision. This parameter adjustment directly resolves the contradiction between measurement precision and energy consumption.
2Measurement precision
If high sampling frequencies are used for KPIs, then measurement precision is improved, but signaling overhead increases
Solution Approach 1:
The system dynamically adjusts the sampling rate based on quantized risk evaluation, making the signaling overhead variable rather than constant. This allows the system to maintain measurement precision when needed while reducing signaling overhead during low-risk periods.
Solution Approach 2:
The sampling rate parameter is changed according to quantized risk evaluation results, directly controlling the amount of signaling data transmitted. This parameter adjustment resolves the contradiction by linking signaling overhead to actual risk levels rather than using a fixed high sampling rate.
3Reliability
If abundant KPIs are monitored, then reliability is improved, but hardware resource consumption increases
Solution Approach 1:
The patent applies local quality by differentiating the sampling rates of different KPIs based on their individual risk evaluations. Instead of uniformly monitoring all KPIs at high frequency, the system applies high sampling rates only to KPIs with high risk levels, while using lower sampling rates for stable KPIs, thus maintaining reliability where needed while reducing overall hardware resource consumption.
Solution Approach 2:
The sampling rate parameter is adjusted for each KPI based on its quantized risk evaluation, allowing the system to monitor multiple KPIs for reliability while optimizing hardware resource usage by reducing sampling rates for low-risk indicators.
4Adaptability or versatility
If abundant KPIs are monitored, then system versatility is improved, but device complexity increases
Solution Approach 1:
The patent segments the KPI monitoring process by dividing KPIs into different risk categories (quantized risk levels). This segmentation allows the RIC to handle diverse KPIs in a structured manner, managing system versatility through organized risk-based groups rather than treating all KPIs uniformly, thereby reducing perceived complexity.
Solution Approach 2:
The system uses quantized risk evaluation to parameterize the monitoring strategy for different KPIs. This parameterization approach maintains system versatility by allowing diverse KPIs to be monitored with appropriate sampling rates while simplifying RIC complexity through a unified risk-based decision framework.
Data Source
AI summary
Various aspects of the present disclosure relate to adaptive monitoring of radio intelligent controller (RIC) key performance indicators (KPIs). An apparatus, such as a RIC, receives performance data of one or more KPIs. The RIC updates a sampling rate of at least one KPI based at least in part on a determination to mitigate use of one or more operational resources of the RIC, where the determination includes a quantized risk evaluation for adjusting the sampling rate of the at least one KPI. The RIC can transmit an indication of an updated sampling rate for the at least one KPI to a network equipment (NE) and/or to a wireless access point for communication to a user equipment (UE).