AI-Driven Interference Management in Wireless RAN Sectors
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Solution Overview
Problem
Wireless networks, such as LTE and 5G, face interference issues due to diverse geographical regions being served by different sets of infrastructure hardware, leading to collisions of radio frequency signals from nearby base stations and unknown interference sources, which degrade RF channel quality and user experience.
Innovation Solution
The implementation of artificial intelligence/machine learning techniques to model sector attributes, determine RF channel quality and interference, and select remedial actions to enhance RF channel quality and reduce interference, by analyzing sector models and interference models using AI/ML methods to dynamically adjust parameters like PRBs, transmit power, and beamforming configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If diverse geographical regions are served by different sets of infrastructure hardware, then network coverage is improved, but interference between nearby base stations increases
Solution Approach 1:
The patent segments the network into multiple sectors with different sector models (e.g., sector model 201-1, 201-2, etc.), each representing different geographical or operational characteristics. This segmentation allows interference management to be tailored to specific sector conditions rather than applying uniform settings across the entire network, thereby reducing interference while maintaining comprehensive coverage.
Solution Approach 2:
The patent implements local quality by determining sector-specific interference models and applying targeted remedial actions to individual sectors or groups of sectors based on their unique characteristics. Each sector can have customized parameters such as PRB assignments, transmit power levels, and beamforming configurations optimized for its specific interference conditions, rather than applying blanket settings across all regions.
2Measurement precision
If manual interference management is performed, then interference reduction accuracy is improved, but operational complexity and time consumption increase
Solution Approach 1:
The patent implements self-service through automated interference management systems that continuously monitor RF metrics, automatically determine interference models, and apply remedial actions without requiring manual intervention. The system autonomously processes sector models, identifies interference patterns, selects appropriate remedial actions, and implements corrections, thereby maintaining high detection accuracy while eliminating time-consuming manual operations.
Solution Approach 2:
The patent incorporates feedback mechanisms where the system continuously monitors RF metrics after applying remedial actions, evaluates the effectiveness of interference reduction, and adjusts subsequent actions based on observed outcomes. This closed-loop feedback enables the system to learn from past interventions and continuously improve interference management precision while operating automatically.
3Device complexity
If generic interference management is applied across all sectors, then system simplicity is maintained, but effectiveness in reducing interference is reduced
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting specific technical parameters (such as PRB assignments, transmit power levels, beamforming configurations) based on determined interference models and sector characteristics. Rather than changing the overall system architecture, the patent modifies operational parameters tailored to each sector's interference conditions, maintaining relative system simplicity while significantly improving interference reduction effectiveness.
Data Source
AI summary
A system described herein may provide for the use of artificial intelligence/machine learning (“AI/ML”) techniques to generate models for various locations or regions (e.g., sectors) associated with one or more radio access networks (“RANs”) of a wireless network. The system may further use AI/ML techniques to generate interference models to reflect types and/or amounts of radio frequency (“RF”) interference measured within the RAN. The system may further determine, based on received RF metrics for a given sector, a particular interference model associated with the sector. Based on a sector model associated with the sector and the determined interference model, one or more actions may be determined in order to remediate any potential interference associated with the sector or surrounding sectors.