Open RAN Overshooting Detection and Automated Cell Recalibration
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Solution Overview
Problem
Overshooting in cellular networks leads to interference, degraded performance, and increased power consumption, requiring manual recalibration prone to human error and costly site visits.
Innovation Solution
Utilizing machine learning models to detect overshooting, recommend optimal cellular transmission distances, and automate adjustments such as antenna tilt or power changes to mitigate overshooting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual recalibration is performed to correct overshooting, then overshooting can be mitigated, but the process is prone to human error and requires costly time-consuming site visits
Solution Approach 1:
The system enables self-service by allowing the network management system to automatically detect overshooting conditions and perform recalibration without requiring human technicians to physically visit sites. The automated system uses machine learning models to identify overshooting and executes mitigation actions independently.
Solution Approach 2:
The patent replaces the mechanical/manual system of physical site visits with an automated digital system. Machine learning models and automated control algorithms substitute for human technicians, enabling remote detection and correction of overshooting conditions through electronic communication with network elements.
2Reliability
If manual recalibration is performed to correct overshooting, then overshooting can be mitigated, but the process is prone to human error
Solution Approach 1:
The patent replaces human manual measurement and judgment with automated machine learning models. These models objectively analyze network data to detect overshooting conditions, eliminating human error in both detection and mitigation decisions.
Solution Approach 2:
The system implements feedback loops where the machine learning models continuously monitor network performance, detect overshooting conditions, execute mitigation actions, and verify results. This closed-loop feedback ensures accurate detection and correction while learning from past performance.
3Productivity
If automated machine learning models are used to detect and mitigate overshooting, then recalibration time is reduced and human error is minimized, but system complexity increases
Solution Approach 1:
The patent implements a universal network management system that performs multiple functions: detecting overshooting, analyzing network data, executing mitigation actions, and verifying results. This multi-functional approach consolidates complexity into a single automated platform rather than requiring separate systems for each task.
Solution Approach 2:
The machine learning models serve as intermediaries between raw network data and mitigation actions. These models process and interpret complex network measurements, translating them into actionable insights that drive automated recalibration decisions.
Data Source
AI summary
Overshooting mitigation 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, based on source cell data applicable to a source cell and based on neighbor cell data applicable to a neighbor cell, determining, using an overshooting classifier model, whether overshooting from the source cell to the neighbor cell has occurred, in response to determining that the overshooting has occurred, determining, using a distance recommendation model, a recommended cellular transmission distance applicable to the source cell, and based on the recommended cellular transmission distance, causing an overshooting mitigation action to be performed that has been determined to conform a cellular transmission distance, applicable to the source cell, to the recommended cellular transmission distance.