ML Access Point Scoring for Network Reliability
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Solution Overview
Problem
User devices often connect to access networks with limited information about available networks, leading to suboptimal choices and reduced network performance due to lack of access to network performance metrics.
Innovation Solution
A scoring platform that receives parameter values associated with access points, generates models based on known quality scores, and determines access point quality scores to guide user devices in selecting networks with improved performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If user devices connect to access networks with limited information, then device complexity is reduced, but network performance deteriorates due to suboptimal access point selection
Solution Approach 1:
A scoring platform is introduced as an intermediary system that collects network performance metrics from multiple access points, processes this information using machine learning models, and generates quality scores. This intermediary resolves the contradiction by providing processed performance information to user devices without requiring devices to directly collect and analyze complex network metrics themselves, thus improving network performance while keeping device complexity low.
Solution Approach 2:
The scoring platform performs preliminary actions by pre-collecting network performance metrics and pre-generating quality scores for multiple access points before user devices need to make connection decisions. This advance preparation of performance information allows devices to quickly select optimal access points without undergoing complex real-time analysis, thereby improving network performance while maintaining simple device operation.
2Reliability
If user devices manually configure connection to access networks, then ease of operation is reduced, but connection reliability is improved through user control
Solution Approach 1:
The system enables self-service by allowing user devices to automatically query the scoring platform for quality scores and autonomously select optimal access points based on this information. This eliminates the need for manual configuration while ensuring connections are made to high-quality networks, as devices independently make informed decisions based on the provided performance metrics.
Solution Approach 2:
The scoring platform provides continuous feedback about access point quality scores to user devices, enabling devices to make informed connection decisions. This feedback mechanism allows devices to automatically configure connections to optimal networks without manual intervention, resolving the contradiction by providing the information needed for reliable automatic selection without requiring manual configuration.
3Reliability
If comprehensive network performance metrics are collected and processed, then network performance improves, but device and network resources are consumed
Solution Approach 1:
The system segments the complex task of network performance analysis by separating data collection, processing, and decision-making functions. The scoring platform collects and processes comprehensive network metrics centrally, while user devices only need to query and act on the generated quality scores. This segmentation improves network performance through thorough analysis while conserving device resources by eliminating the need for devices to perform complex metric processing locally.
Solution Approach 2:
Instead of requiring each user device to independently collect and process comprehensive network performance metrics, the system creates a centralized copy of this information through the scoring platform. Devices access the processed quality scores as a simplified representation of the full performance data, which improves network performance through comprehensive analysis while significantly reducing the computational resources needed at each device.
Data Source
AI summary
A device may receive information that identifies a first set of parameter values associated with a first set of access points. The first set of access points may be associated with a set of known access point quality scores. The device may generate a model based on the set of known access point quality scores and the first set of parameter values. The device may receive information that identifies a second set of parameter values associated with a second set of access points. The device may determine a set of access point quality scores, for the second set of access points, based on the second set of parameter values and the model. The device may provide information to permit an action to be performed in association with the second set of access points.