Predictive Handover for Mobile Radio Networks Using Observer Vehicles
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Solution Overview
Problem
In mobile networks, the handover process for vehicles often results in inefficient cell power adjustments and increased energy consumption due to incorrect cell selection during handovers, especially in scenarios with legacy vehicles, leading to superfluous eNodeB processes and network throughput issues.
Innovation Solution
A method where observer vehicles equipped with surroundings observation means, such as LIDAR, RADAR, and video cameras, predict the travel route and position of road participants and inform the base station, allowing the core network management component to associate this information with user equipment identities, thereby preparing the correct cell for handover and reducing incorrect power adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If traditional handover procedures based on field strength measurements are used, then handover decisions are made automatically, but incorrect cell selection occurs leading to superfluous eNodeB power adjustments and increased energy consumption
Solution Approach 1:
The system performs preliminary actions by having observer vehicles predict future positions and routes of road participants before handover is needed. The core network management component uses this advance information to pre-identify the correct target cell, preventing incorrect cell selection and avoiding superfluous eNodeB power adjustments that would waste energy.
Solution Approach 2:
The patent introduces an intermediary system consisting of observer vehicles with prediction algorithms and a core network management component. This intermediary layer processes position and route information to determine the correct target cell, acting as a mediator between traditional field strength measurements and handover execution, thereby improving cell selection accuracy and reducing energy waste from incorrect selections.
2Reliability
If observer vehicles predict travel routes and inform base stations, then correct cell preparation for handover is achieved, but system complexity increases due to additional vehicles and communication protocols
Solution Approach 1:
The patent applies universality by making ordinary vehicles serve multiple functions: they continue their primary transportation role while simultaneously acting as observer vehicles with prediction capabilities. This multi-functionality allows the system to gain reliability improvements without adding dedicated infrastructure, as regular vehicles provide the additional observation and prediction functions needed for accurate handover preparation.
Solution Approach 2:
The system implements self-service by enabling vehicles to autonomously predict their own and other vehicles' routes using onboard sensors and algorithms. The prediction and information sharing are performed automatically without requiring manual intervention or complex centralized control, thereby improving handover reliability while minimizing the increase in system complexity.
3Productivity
If eNodeBs perform power adjustments based on predicted handovers, then network throughput is optimized, but incorrect predictions lead to unnecessary power boots and reduced efficiency
Solution Approach 1:
The system implements feedback mechanisms where observer vehicles continuously monitor and report actual routes and positions. The core network management component compares predicted routes with actual trajectories, using this feedback to refine predictions and adjust power settings accurately. This feedback loop ensures eNodeBs perform power adjustments only when genuinely needed, optimizing network throughput while preventing energy waste from incorrect predictions.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach reduces energy consumption and enhances network throughput by accurately predicting vehicle movements and preparing the correct cell for handover, minimizing the need for unnecessary eNodeB power boots and improving the efficiency of handover processes.
Implementation Method 1
observer vehicles equipped with surroundings observation means, such as LIDAR, RADAR, and video cameras
Implementation Method 2
observer vehicles equipped with surroundings observation means, such as LIDAR, RADAR, and video cameras
Data Source
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AI summary
The proposal concerns a method for performing a handover process for a mobile radio network terminal in a mobile radio network. Said mobile radio network terminal might be a user equipment device of a passenger of a vehicle (14). The method comprises the steps of observing the vehicle (14) with surroundings observation means, and predicting a travel route for said observed vehicle (14). Furthermore it comprises a step of informing the base station (210c) to which said vehicle (12) with surroundings observation means is logged on about the predicted travel route. Such base station (210c) forwards the information about the predicted travel route to the base station (210a) of the cell (C1) to which the user equipment device in said observed vehicle (14) is logged on. In a core network management component (220), to which this information is forwarded, an evaluation of said information about the predicted travel route takes place. Hence, the base station (210b, 210c) of said cell (C2, C3) to which the observed vehicle (14) is travelling according to the predicted travel route is informed to prepare for taking over said user equipment from the passenger in the observed vehicle (14) in said handover process.