UAV Network Slice Assignment and Beam Management
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Solution Overview
Problem
Providing reliable data services to Unmanned Aerial Vehicles (UAVs) while in flight is challenging due to the dynamic nature of UAV movements and the need for efficient network resource management.
Innovation Solution
The system detects UAVs, assigns network slices based on the UAV's type and usage, and performs antenna beam management using historical data and machine learning to ensure continuous network connectivity during flight.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional network management approaches are used for UAVs, then network resource allocation is simple, but reliable data services cannot be ensured during dynamic flight movements
Solution Approach 1:
The patent applies segmentation by dividing the network into multiple network slices, each optimized for specific UAV applications (e.g., autonomous flight, video streaming, telemetry). This allows different QoS parameters to be allocated to different slices, ensuring reliable data services for critical functions while managing overall network complexity through structured division.
Solution Approach 2:
The patent implements dynamic network slice selection and beam management that adapts to real-time UAV flight conditions. The system continuously monitors UAV position, velocity, and service requirements to dynamically allocate network resources and switch between slices, ensuring reliable connectivity during dynamic flight movements while maintaining manageable complexity through automated decision-making algorithms.
2Reliability
If network resources are allocated dynamically to track UAV movements, then service reliability improves, but network resource management complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-configuring multiple network slices with different QoS characteristics before UAV deployment. The system prepares beamforming configurations and resource allocation schemes in advance for various flight scenarios, enabling rapid adaptation to real-time conditions without requiring complex real-time optimization calculations, thus improving connectivity reliability while managing complexity.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously monitors UAV flight status, signal quality, and service performance. Based on this feedback, the network dynamically adjusts beam directions, switches between network slices, and reallocates resources. This closed-loop control ensures reliable connectivity while maintaining manageable complexity through rule-based decision algorithms that respond to measured conditions.
3Adaptability or versatility
If multiple network slices are assigned to different UAV types, then service adaptability improves, but network configuration complexity increases
Solution Approach 1:
The patent applies local quality by assigning specific QoS characteristics and resource allocations to different network slices tailored to specific UAV application requirements. For example, autonomous flight receives slices with low latency and high reliability, while video streaming receives slices with high bandwidth. This localized optimization improves service adaptability while the modular slice structure keeps overall configuration complexity manageable through standardized templates.
Data Source
AI summary
A method may include transmitting multiple antenna beams to an unmanned aerial vehicle (UAV) and determining a location of the UAV. The method may also include identifying a network slice to service the UAV, assigning the identified network slice to the UAV and performing antenna beam management for the UAV while the UAV is in flight.