AI Uplink Performance Recommender for Neighboring Cell Conflicts
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Solution Overview
Problem
Current methods for optimizing LTE uplink performance in wireless networks are limited by the need for manual analysis of large datasets, reliance on pre-determined thresholds, and lack of scalability, leading to inconsistent and resource-intensive trial deployments.
Innovation Solution
An AI-based uplink performance recommender system that utilizes machine learning to analyze network data, identify root causes, and recommend configuration changes, while resolving conflicts between neighboring cells to optimize uplink performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual analysis of large datasets is used for optimizing LTE uplink performance, then current methods can be implemented with existing tools, but the process becomes resource-intensive and time-consuming
Solution Approach 1:
The patent replaces manual mechanical analysis of network data with an AI-based automated system. The machine learning model automatically processes large datasets, identifies performance issues, and generates optimization recommendations, eliminating the need for manual data analysis and reducing trial deployment time while maintaining optimization accuracy
Solution Approach 2:
The system performs self-service by automatically analyzing network performance data, identifying root causes of uplink issues, and generating optimization recommendations without requiring manual intervention. The AI model continuously learns from network data and autonomously provides optimization strategies, significantly reducing the time and resources needed for trial deployments
2Adaptability or versatility
If pre-determined thresholds are used for optimization, then the optimization process is simple to implement, but it lacks adaptability to diverse network conditions
Solution Approach 1:
The patent changes the optimization approach from fixed pre-determined thresholds to dynamic parameter adjustments based on machine learning. The AI model analyzes network data and automatically determines optimal parameter values tailored to specific network conditions, enabling adaptability to diverse scenarios while managing complexity through automated decision-making
Solution Approach 2:
The system incorporates feedback mechanisms where the AI model continuously monitors network performance, compares actual results with predicted outcomes, and adjusts optimization recommendations accordingly. This feedback loop enables the system to adapt to changing network conditions and learn from past optimizations, improving versatility without requiring complex manual reconfiguration
3Reliability
If iterative trials are conducted for optimization, then configuration changes can be tested empirically, but the process becomes resource-intensive and non-scalable
Solution Approach 1:
The patent uses the copying principle by creating a virtual replica of the network environment through machine learning models. Instead of conducting physical iterative trials in the actual network, the AI model simulates and predicts the impact of configuration changes, allowing reliable optimization testing without the resource intensity and scalability limitations of empirical trials
Solution Approach 2:
The system replaces the mechanical process of iterative physical trials with an AI-based predictive system. The machine learning model analyzes network data, simulates potential optimizations, and provides recommendations with predicted outcomes, achieving both reliability in optimization accuracy and scalability in productivity without requiring repeated empirical testing
4Productivity
If automated AI-based optimization is implemented, then scalability and efficiency improve, but the system complexity increases
Solution Approach 1:
The patent applies universality by designing a multi-functional AI-based system that performs multiple tasks: data collection, performance analysis, root cause identification, and optimization recommendation generation. This consolidated multi-functional approach improves productivity and scalability while managing complexity by combining multiple functions in a single integrated system rather than requiring separate specialized components
Data Source
AI summary
The invention refers to a method performed by a performance recommender for a wireless network, obtaining (2010) for a plurality of cells input data, the input data comprising actual cell configuration parameter values; applying (2030) a machine-learning model to the input data to generate, for at least a portion of the cells, one or more recommendations for changes to the cell configuration parameter values to improve uplink, UL, performance in the respective cells; and based on identifying conflicts between recommendations for different cells, partitioning (2040) the plurality of cells into a plurality of interaction areas of neighboring cells; resolving (2050) conflicts in recommendations for respective cells within each of the interaction areas and across different interaction areas; and for at least a portion of the cells, determining (2060) preferred values for the cell configuration parameters to improve UL performance in the respective cells; the invention further relates to a corresponding performance recommender and a corresponding computer program.