Open RAN Distributed Unit Failure Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Open radio access networks face challenges in predicting and proactively addressing failures and performance degradation in distributed units, leading to potential service disruptions and inefficiencies in maintenance and upgrades.
Innovation Solution
A method utilizing machine learning models built from telemetry data to predict failures and performance degradation, enabling proactive remedial actions such as graceful shutdowns and autonomous upgrades, ensuring minimal service disruption and efficient resource management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional reactive maintenance is used for distributed units, then service disruptions occur when failures happen, but implementing proactive predictive maintenance requires complex machine learning models and continuous telemetry data processing
Solution Approach 1:
The system performs preliminary actions by continuously monitoring telemetry data and predicting failures before they occur. The machine learning model analyzes historical and real-time data to identify patterns indicating upcoming failures, enabling the system to take preventive maintenance actions during low-utilization periods rather than waiting for actual failures to disrupt service.
Solution Approach 2:
The maintenance system performs self-service through autonomous operation. The machine learning model automatically processes telemetry data, predicts failures, and triggers remedial actions without human intervention. The system monitors its own distributed units and self-manages the predictive maintenance process, reducing the need for external monitoring and manual maintenance scheduling.
2Productivity
If distributed units are shut down for maintenance or upgrades, then service continuity is disrupted, but performing maintenance during low-utilization periods requires continuous monitoring and dynamic decision-making
Solution Approach 1:
The system applies dynamics by continuously adapting maintenance scheduling based on real-time utilization monitoring. Instead of fixed maintenance schedules, the system dynamically adjusts timing based on current traffic conditions, utilizing periods of low demand identified through continuous telemetry analysis. This dynamic approach allows maintenance to be performed when it has minimal impact on service continuity while maximizing maintenance efficiency.
Solution Approach 2:
The system uses feedback mechanisms by continuously monitoring utilization metrics and adjusting maintenance scheduling accordingly. Telemetry data provides real-time feedback on network conditions, which feeds back into the machine learning model to determine optimal maintenance timing. This closed-loop feedback ensures maintenance actions are coordinated with actual network demand patterns, maintaining service continuity while improving maintenance efficiency.
3Loss of time
If machine learning models continuously analyze telemetry data to predict failures, then early detection of issues is achieved, but data processing requirements and computational resources increase
Solution Approach 1:
The system applies partial action by focusing computational resources on analyzing only the most critical and predictive telemetry parameters rather than processing all available data equally. The machine learning model identifies and prioritizes key indicators of potential failures, performing deep analysis on these high-value parameters while using lighter processing for routine monitoring data, thus reducing overall computational resource consumption while maintaining early detection capability.
Data Source
AI summary
A disclosed method may include (i) building, based on telemetry data from an open radio access network, a machine learning model that predicts when a candidate distributed unit within the open radio access network will experience a failure, (ii) detect, by applying the machine learning model that predicts when the candidate distributed unit will shut down, that a specific distributed unit will experience a specific failure, and (iii) perform, in response to detecting that the specific distributed unit will experience the specific failure, a remedial action that addresses the specific failure. Related systems and computer-readable mediums are further disclosed.