QoE-Based Handoff Policy for Mobility Networks
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Solution Overview
Problem
Conventional cellular handover policies in mobility networks are static and do not consider user quality of experience (QoE), leading to suboptimal handover decisions, especially when users are handed over to congested cells.
Innovation Solution
A novel handoff policy based on user QoE is introduced, which captures spatial-temporal information about cell site operation and detects abnormal cells. This policy rejects handover requests to congested cells and selects alternative cells based on the user's traffic demand and application type, maintaining a handoff log to avoid loops.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a static handover policy based on signal strength threshold is used, then the handover decision process is simple and fast, but the user quality of experience deteriorates when handed over to congested cells
Solution Approach 1:
The patent transforms the static handover policy into a dynamic one by continuously monitoring cell usage levels and adapting handover decisions in real-time. The system determines whether a target cell is in a congested state and adjusts handover behavior accordingly, making the policy flexible and responsive to changing network conditions rather than relying on fixed signal strength thresholds alone
Solution Approach 2:
The system implements feedback mechanisms by capturing spatial-temporal information about cell site operation and usage levels. This feedback loop allows the network to detect abnormal cells and use this information to make informed handover decisions, preventing users from being handed over to congested cells while maintaining manageable system complexity through structured information collection and processing
2Reliability
If handover requests are rejected to avoid congested cells, then user quality of experience is improved, but the handover decision process becomes more complex requiring traffic demand inference
Solution Approach 1:
The system uses an intermediary approach by inferring traffic demand indirectly from observable user operations and application types rather than directly measuring internal device state. This mediation layer translates visible user behavior into traffic demand classification, enabling congested cell avoidance decisions without requiring direct access to device traffic metrics
Solution Approach 2:
The system performs preliminary classification of user traffic demand into performance-sensitive and performance-tolerant categories before making handover decisions. By pre-categorizing users based on their application types and operational patterns, the system prepares handover rejection decisions in advance for congested cells, improving handover success rates while managing detection complexity through proactive classification
Data Source
AI summary
Aspects of the subject disclosure may include, for example, receiving a handover request from a user equipment (UE) device in a mobility network, wherein the handover request identifies a target cell for handing over radio communication with the UE device from a source cell, wherein the identifying is based on the handover request, determining a usage level of the UE device, wherein the usage level comprises one of performance-sensitive traffic and performance-tolerant traffic, selecting an alternative target cell for the handover request, wherein the selecting is responsive to determining a performance-sensitive traffic usage level of the UE device, and initiating a handover operation between the source cell and the alternative target cell. Other embodiments are disclosed.