Predictive QoS Control for Moving User Equipment Handover
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Solution Overview
Problem
Conventional solutions in 5G networks struggle to provide sustained quality of service (QoS) to moving vehicles and drones, particularly during handovers, as they fail to effectively manage congestion and prioritize traffic based on user equipment type and path of travel.
Innovation Solution
A system and method that includes a location module to identify user equipment, an analysis module to predict paths and determine cell loads, and a traffic action module to shape traffic and prioritize based on congestion levels, ensuring QoS sustainability by anticipating and managing cell transitions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional 5G network solutions are used for moving vehicles and drones, then network coverage is provided, but quality of service cannot be sustained during handovers due to congestion management failures
Solution Approach 1:
The system performs preliminary actions by predicting the path of travel for moving user equipment and identifying future cells along that path before handover occurs. Traffic actions are determined in advance for these predicted cells, allowing the network to proactively manage congestion and prioritize traffic for vehicles and drones before they enter congested areas, thereby sustaining QoS during handovers.
2Reliability
If traffic actions are provided for all cells in the network, then congestion management is comprehensive, but network complexity and processing overhead increase
Solution Approach 1:
The system applies local quality by determining traffic actions only for specific predicted cells along the path of travel of moving user equipment, rather than uniformly across all network cells. This targeted approach focuses computational resources on cells that will actually serve vehicles and drones, reducing overall network processing complexity while maintaining effective congestion management where needed.
3Ease of operation
If manual intervention is used for QoS management, then traffic prioritization can be customized, but response time and operational efficiency decrease
Solution Approach 1:
The system implements self-service by automatically identifying moving user equipment, predicting their path of travel, determining cell loads, and providing appropriate traffic actions without manual intervention. The system autonomously prioritizes traffic for vehicles and drones based on predicted congestion levels and QoS requirements, achieving both customization and rapid response times through automated decision-making.
4Reliability
If path prediction and proactive traffic management are implemented, then QoS is sustained during handovers, but system complexity and computational requirements increase
Solution Approach 1:
The system uses path prediction to perform preliminary actions by identifying future cells along the predicted path of moving user equipment before handover occurs. Traffic actions are determined in advance for these predicted cells based on load conditions, allowing the network to proactively prepare and maintain QoS during handovers without requiring complex real-time adjustments during the actual handover event.
Data Source
AI summary
A method for delivering quality of service to a moving user equipment in a computer network, the method including: identifying a moving user equipment on the computer network; predicting a path of travel for the user equipment; determining a load of a cell in the path of travel; determining a traffic action response based on the load of the cell; and providing the traffic action. A system for delivering quality of service to a moving user equipment in a computer network, the system including: a location module configured to identify a moving user equipment on the computer network; an analysis module configured to predict a path of travel for the user equipment; a load module configured to determine a load of a cell in the path of travel; and a traffic action module configured to determine a traffic action response based on the load of the cell and provide for the traffic action.