AI-Assisted PIM Detection and Scheduled RF Interference Correction
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Solution Overview
Problem
Passive intermodulation (PIM) interference is a growing issue in cellular networks, particularly with the introduction of 5G technology, causing significant network capacity and coverage deterioration, and existing detection and cancellation methods are inadequate for real-time, dynamic non-linearity characteristics.
Innovation Solution
An AI/ML-powered PIM detection system is introduced alongside traditional detection, with a framework that includes an Offline AI/ML Model training management (OAMTM) and decoupling of PIM correction functionality into a controller (PIMC-C) and processor (PIMC-P), enabling continuous model training and accurate, real-time PIM detection and correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional PIM detection methods are used, then the system structure is simple, but the detection accuracy and efficiency are insufficient for real-time dynamic non-linearity characteristics
Solution Approach 1:
The patent divides the PIM correction functionality into two separate units: a PIM correction controller and a PIM correction processor. This segmentation allows the detection system to focus on accurate PIM identification while the correction functions are handled independently, improving overall detection accuracy without overwhelming system complexity.
Solution Approach 2:
The patent introduces an AI/ML-powered intermediary detection system that acts as a mediator between traditional detection methods and PIM correction. This intermediary analyzes signals using machine learning models to accurately identify PIM components, improving detection precision while managing complexity through specialized intermediate processing.
2Reliability
If PIM correction is activated continuously, then PIM impact is reduced, but energy consumption and system resource usage increase
Solution Approach 1:
The patent implements periodic PIM detection and correction activation based on scheduled time intervals rather than continuous operation. The system detects PIM at specific periods and activates correction only when needed, maintaining network reliability while significantly reducing energy consumption compared to continuous correction.
Solution Approach 2:
The system uses AI/ML models that automatically learn and adapt to PIM patterns, enabling self-service operation. The detection system autonomously identifies PIM conditions and triggers correction only when necessary, improving reliability through intelligent automation while minimizing energy waste through condition-based activation.
3Productivity
If AI/ML-powered detection is implemented, then detection efficiency and accuracy improve, but device complexity and computational requirements increase
Solution Approach 1:
The patent segments the AI/ML detection functionality into a separate detection unit that operates independently from the correction unit. This allows high-efficiency AI-based detection to be implemented without burdening the entire system with unnecessary complexity, as only the detection portion requires advanced computational resources.
Solution Approach 2:
The AI/ML-powered detection system serves as an intermediary layer between signal reception and PIM correction. It efficiently processes signals to identify PIM components using machine learning, then passes results to the correction unit, improving overall detection efficiency while containing complexity within the intermediary detection layer.
4Quantity of substance
If multiple carriers are collocated to increase network capacity, then network capacity increases, but PIM interference increases
Solution Approach 1:
The patent implements a feedback mechanism where the AI/ML detection system continuously monitors signals from multiple collocated carriers, identifies PIM interference patterns, and provides feedback to the correction unit. This feedback loop enables the system to maintain high network capacity from multiple carriers while actively compensating for PIM interference through scheduled correction.
Solution Approach 2:
The patent converts the harmful PIM interference generated by multiple collocated carriers into beneficial information for training and improving the AI/ML detection model. By analyzing actual PIM patterns from multi-carrier operations, the system learns to better identify and correct interference, ultimately improving network capacity utilization while managing PIM effects.
Data Source
AI summary
The disclosure relates to a method, apparatus and computer readable media, for detecting and correcting passive intermodulation (PIM). The method comprises receiving a radio frequency (RF) signal. The method comprises providing the RF signal to a first and a second PIM detectors. The method comprises, upon detection of PIM by at least one of the first and second PIM detectors, for which, based on a schedule, the detection of PIM is applicable at a time of detection of PIM, activating PIM correction.