Crowd-Sourced Access Point Data for 5G Network Planning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current network planning for 5G and next-generation networks faces challenges in efficiently managing signal transfer thresholds, particularly for real-time services like VoLTE and VoWiFi, due to unpredictable Wi-Fi jitter caused by interference and unmanaged access points, leading to suboptimal service quality and resource wastage.
Innovation Solution
The implementation of a crowd-sourced access point data collection system that uses user equipment (UE) to gather and share access point quality and threshold information, enabling a centralized database to optimize network selection by identifying problematic access points and adjusting thresholds dynamically, thereby improving service quality and reducing resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional network planning methods are used with fixed signal transfer thresholds, then network configuration is simple, but service quality deteriorates due to unpredictable Wi-Fi jitter and interference
Solution Approach 1:
The system performs preliminary measurements of signal strength and jitter characteristics across the network before finalizing threshold configurations. Network devices collect and analyze historical performance data in advance, allowing thresholds to be pre-optimized for different scenarios before actual service deployment, thereby improving reliability without requiring complex real-time adjustments
Solution Approach 2:
Signal transfer thresholds are made dynamic rather than fixed. The system continuously monitors Wi-Fi jitter, interference levels, and signal quality metrics, automatically adjusting thresholds in response to changing network conditions. This dynamic adaptation ensures service quality is maintained across varying environmental conditions without manual intervention
2Adaptability or versatility
If signal transfer thresholds are lowered to improve service coverage, then more access points become available, but resource wastage increases due to unnecessary transfers and jitter-related issues
Solution Approach 1:
The system implements feedback mechanisms where network devices monitor the performance outcomes of access point selections. When transfers to Wi-Fi access points result in jitter-related failures or poor service quality, the system learns from this feedback and adjusts future selection decisions, avoiding repeated wasteful transfers while maintaining flexibility to select from multiple access points based on real-time conditions
Solution Approach 2:
The system changes the parameters used for access point selection beyond simple signal strength thresholds. It incorporates jitter characteristics, interference levels, and historical performance metrics into the selection criteria, allowing the system to maintain flexible access point selection while avoiding transfers to problematic networks that would waste resources
3Reliability
If crowd-sourced data collection is implemented to optimize network planning, then service quality improves through better threshold optimization, but device complexity and data management requirements increase
Solution Approach 1:
Network devices autonomously collect, measure, and report their own performance data without requiring centralized configuration or manual intervention. Each device independently generates crowd-sourced data about signal quality, jitter, and transfer performance, eliminating the need for complex external data management systems while improving network planning accuracy through aggregated real-world measurements
Data Source
AI summary
Network planning based on collected crowd-sourced access point quality and selection data can optimize access point frequency and/or bandwidth selection. A cloud-based application can be utilized in conjunction with a 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 at a cloud platform, which creates an access point performance and selection threshold profile from which the network can facilitate access point frequency and/or bandwidth selections.