UE Mobility Prediction for Wireless Resource Allocation
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Solution Overview
Problem
Conventional wireless networks face inefficiencies in resource allocation due to latency in reacting to real-time changes in traffic density and resource utilization, leading to sub-optimal admission and resource allocation decisions.
Innovation Solution
The development of methods and systems that predict user equipment (UE) mobility by gathering statistical information to build migration probability tables, allowing for more accurate resource provisioning and dynamic adjustment of handover margins based on estimated migration probabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional reactive resource allocation is used, then system simplicity is maintained, but resource utilization efficiency deteriorates due to latency in reacting to real-time changes
Solution Approach 1:
The system performs preliminary actions by predicting future UE locations and resource requirements before actual handovers occur. Migration probability tables are built in advance based on historical mobility patterns, enabling proactive resource allocation decisions that eliminate reactive latency.
Solution Approach 2:
The system transitions from static, reactive resource allocation to dynamic, predictive allocation. Handover margins and resource reservations are continuously adjusted based on real-time mobility predictions and changing network conditions, allowing the system to adapt proactively to UE movements.
2Reliability
If larger bandwidth reserves are maintained to accommodate traffic fluctuations, then call blocking is reduced, but resource utilization efficiency deteriorates due to wasted reserved capacity
Solution Approach 1:
The system dynamically changes handover margin parameters based on predicted migration probabilities rather than maintaining fixed, conservative reserves. By adjusting these parameters in real-time according to actual mobility patterns, the system optimizes the balance between call blocking prevention and resource utilization efficiency.
3Productivity
If reactive handover initiation is used, then system complexity is minimized, but resource allocation optimality deteriorates due to delayed response to UE mobility
Solution Approach 1:
The system implements self-service through automated mobility prediction and resource allocation. UE mobility patterns are automatically analyzed, migration probability tables are dynamically built, and handover decisions are made autonomously based on predicted locations, eliminating the need for complex manual intervention while optimizing resource allocation.
Data Source
AI summary
Predicting mobile station migration between geographical locations of a wireless network can be achieved using a migration probability database. The database can be generated based on statistical information relating to the wireless network, such as historical migration patterns and associated mobility information (e.g., velocities, bin location, etc.). The migration probability database consolidates the statistical information into mobility prediction functions for estimating migration probabilities/trajectories based on dynamically reported mobility parameters. By example, mobility prediction functions can compute a likelihood that a mobile station will migrate between geographic regions based on a velocity of the mobile station. Accurate mobility prediction may improve resource provisioning efficiency during admission control and path selection, and can also be used to dynamically adjust handover margins.