Network Equipment Parameter Adjustment via Machine Learning
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Solution Overview
Problem
Conventional systems lack a mechanism to automatically identify adjustments to network equipment parameters that would improve overall telecommunications network performance, as improving one key performance indicator (KPI) may degrade another, and there are hundreds of inter-dependent KPIs, making it difficult to discern which adjustments would result in improved network performance.
Innovation Solution
A network equipment operation adjustment system generates a network score representing the performance of a telecommunications network within a geographic region, determines necessary parameter adjustments using the network score, and applies these adjustments to improve performance, utilizing a machine learning model to identify optimal reconfigurations of network equipment and components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If network equipment parameters are manually configured to optimize one KPI, then that specific KPI improves, but other inter-dependent KPIs may deteriorate
Solution Approach 1:
The system enables network equipment to automatically adjust its own parameters based on real-time KPI monitoring and machine learning model recommendations, eliminating the need for manual configuration and reducing operational complexity while maintaining optimal performance across multiple inter-dependent KPIs
Solution Approach 2:
The system implements a closed-loop feedback mechanism where KPIs are continuously monitored, analyzed by the machine learning model, and used to automatically adjust network equipment parameters, ensuring that optimizations in one area do not negatively impact other inter-dependent KPIs
2Reliability
If manual monitoring and adjustment of hundreds of KPIs is performed, then network performance can be optimized, but the time and resources required increase significantly
Solution Approach 1:
The system replaces manual mechanical processes of KPI monitoring and analysis with an automated machine learning model that processes hundreds of KPIs in real-time, dramatically reducing the time and human resources required while maintaining or improving optimization quality
Solution Approach 2:
The machine learning model acts as an intermediary between raw KPI data and network equipment parameter adjustments, automatically analyzing hundreds of inter-dependent KPIs and determining optimal parameter settings without requiring human intervention or interpretation
3Ease of manufacture
If network equipment is initially configured for optimal performance under assumed conditions, then deployment is simplified, but the system becomes unable to adapt when actual network conditions differ from assumptions
Solution Approach 1:
The system transitions from static initial configuration to dynamic adaptive operation, where network equipment parameters are continuously adjusted based on real-time KPI monitoring and machine learning model recommendations, enabling the system to adapt to changing network conditions while maintaining ease of initial deployment
Data Source
AI summary
A network equipment operation adjustment system is provided herein that is configured to improve the performance of a telecommunications network by generating a network score representing the performance of a telecommunications network within a geographic region, determining one or more network equipment parameter adjustments using the network score, and causing the adjustments to occur. The network equipment operation adjustment system can further display the network score and other network scores for other geographic regions in an interactive user interface to efficiently allow a network operator to view the network performance of a telecommunications network by geographic region and/or to view how the network performance in each of the geographic regions is changing over time.