Cellular Network Mobility Optimization via ML Quality Reports
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Solution Overview
Problem
Current cellular network load balancing methods rely on latency and throughput, which do not accurately represent the actual service quality for user devices during handovers, leading to suboptimal communication experiences.
Innovation Solution
Implementing a system that utilizes user device self-reported quality metrics and machine learning mechanisms to generate quality reports for each cell, predicting the best communication quality for user devices by collecting and analyzing reference-signal-received-power, reference-signal-received-quality, and channel-quality-indicator values, enabling informed handover decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If load balancing methods use latency and throughput as factors for cell selection, then network traffic distribution is simplified, but the accuracy of service quality representation deteriorates
Solution Approach 1:
The patent introduces quality reports as an intermediary mechanism between cell measurements and load balancing decisions. These quality reports, generated by processing user device self-reported quality metrics through machine learning models, serve as a mediator that translates raw measurement data into actionable service quality predictions, resolving the contradiction between simplified processing and accurate representation
Solution Approach 2:
The patent transforms the parameter set from traditional network metrics (latency, throughput) to user-perceived quality metrics (reference signal received power, reference signal received quality, channel quality indicator). This parameter transformation enables more accurate service quality representation while maintaining manageable system complexity through automated processing
2Speed
If traditional load balancing factors are used for cell handover decisions, then decision-making speed is maintained, but handover optimization deteriorates
Solution Approach 1:
The system performs preliminary action by pre-processing and storing user device self-reported quality metrics in quality reports before handover decisions are needed. This advance preparation enables fast decision-making while improving reliability, as the quality reports are updated continuously with historical data that reflects actual user experience patterns
Solution Approach 2:
The patent implements feedback mechanisms where user devices continuously report their perceived quality metrics to the network. These feedback signals are processed through machine learning models to generate updated quality reports, creating a closed-loop system that continuously optimizes handover decisions based on actual user experience rather than theoretical metrics
3Measurement precision
If user device self-reported quality metrics are collected and processed through machine learning, then service quality prediction accuracy is improved, but system complexity increases
Solution Approach 1:
The patent applies self-service by having user devices autonomously measure and report their own quality metrics without requiring complex network-side measurement systems. The devices self-monitor their connection quality and communicate this information to the network, simplifying the overall system architecture while maintaining high measurement accuracy
Solution Approach 2:
The system uses machine learning models that learn from historical quality data to create simplified representations (copies) of complex network conditions. These models translate raw self-reported metrics into predictive quality reports, reducing processing complexity while maintaining prediction accuracy through learned patterns rather than complex calculations
Data Source
AI summary
Systems and methods to utilize user device self-reported quality metrics and machine learning mechanisms to optimize management of user device mobility in a cellular network. Cells in the network obtain quality data for one or more user devices in communication with the cells, including channel-quality-indicator values, reference-signal-received-power values, and reference-signal-received-quality values. The quality data is processed by a machine learning mechanism to generate a separate quality report for each cell. In response to receiving a request to handover communications for a target user device, the quality reports are utilized to select a cell that is predicted to provide the best quality communications for the target user device.