Crowd-sourced Access Point Jitter Database for 5G Network Selection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current Wi-Fi selection mechanisms for voice over Wi-Fi services are inadequate due to their reliance on received signal strength indicator (RSSI) measurements, which fail to account for jitter, leading to suboptimal network performance and increased battery drain as devices repeatedly test access points for quality, limiting the utility of crowd-sourced access point data.
Innovation Solution
A system that consolidates user equipment (UE)-collected access point jitter measurements and thresholds in a cloud database, allowing for optimized signal strength and jitter adjustments, reducing the need for frequent measurements by caching and updating access point lists, and enabling broader network efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If Wi-Fi selection mechanisms rely on RSSI measurements, then implementation is simple, but network performance deteriorates due to failure to account for jitter
Solution Approach 1:
The patent changes the measurement parameters from simple RSSI to composite metrics including jitter, packet loss, and latency. The system collects multiple quality parameters and uses them to dynamically adjust selection thresholds, transforming the decision-making process from single-parameter to multi-parameter evaluation, thereby improving network performance while maintaining implementation feasibility through automated threshold optimization.
Solution Approach 2:
The patent implements feedback mechanisms where devices report measured quality parameters back to the network, and the system uses this feedback to dynamically adjust selection thresholds. The thresholds are not fixed but are continuously optimized based on actual network conditions and device reports, creating a closed-loop system that adapts to changing network environments and improves overall performance.
2Measurement precision
If devices repeatedly test access points for quality, then network selection accuracy improves, but battery drain increases
Solution Approach 1:
The patent performs preliminary actions by pre-calculating and establishing optimized selection thresholds before actual network selection is needed. These thresholds are derived from historical data and crowd-sourced information about access point quality, allowing devices to make informed selection decisions without repeatedly performing full quality tests, thereby reducing battery consumption while maintaining accuracy.
Solution Approach 2:
The patent uses copies of quality data from multiple sources including crowd-sourced measurements and network reports to inform selection decisions. Instead of each device performing complete quality assessments from scratch, the system leverages existing measured data and thresholds as references, reducing the computational and energy burden on individual devices while maintaining selection accuracy.
3Quantity of substance
If access point data is collected from multiple users, then database completeness improves, but data management complexity increases
Solution Approach 1:
The patent segments the data management task by organizing access point data into structured categories and hierarchies. The system divides the complex data into manageable components such as quality metrics, spatial information, temporal patterns, and device-specific parameters, allowing for systematic storage, retrieval, and processing while reducing the perceived complexity through structured organization.
Solution Approach 2:
The patent creates a universal data collection and management framework that serves multiple functions simultaneously. The same infrastructure collects data from diverse sources, stores it in standardized formats, processes it for threshold optimization, and makes it available for network selection decisions. This multi-functional approach consolidates what would otherwise be separate complex systems into a single unified platform.
Data Source
AI summary
Collection of crowd-sourced access point quality and selection data for intelligent network selection can be utilized by mobile devices to self-learn and optimize access point device selection. A cloud-based application can be utilized in conjunction with the mobile device to build a database of access point quality and thresholds suitable for real-time and other jitter-sensitive services. The mobile device jitter measurements and selection thresholds can be collected and sent to a cloud platform, which creates an access point performance and selection threshold profile.