Network Slicing 5G for Dedicated Aerial Corridors

Method and apparatus for allocating and managing a service area for a user equipment (UE) in a wireless communication system, particularly for unmanned aerial systems (UAS), by identifying aviation-enabled cells and controlling handovers based on flight-related information and cell capabilities. The method involves receiving flight information from a UAS service supplier, determining candidate base stations, requesting aviation-related information, generating a handover list, and controlling UE handovers.

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Enhancing geospatial mobility management of unmanned aerial vehicles (UAVs) in 5G networks to enable seamless high-speed transit of drones between cells without disruption. The method involves mapping UAVs to geographic regions and instantiating network slices associated with the UAV and regions. This allows generating partially determined flight paths that consider the slices and regions. The UAV then executes the path transitioning between slices as needed. This avoids mobility registration updates for each cell by pre-planning and coordinating the handoffs.

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Unmanned aerial vehicle (UAV) wireless backhaul system for establishing 5G networks in emergency situations. The system enables rapid deployment of 5G networks using drone technology, with the UAV serving as a mobile base station that establishes connections between user equipment and radio towers. The system integrates power transfer capabilities through a tethered station, allowing continuous operation while airborne. The UAV's onboard UBR antenna enables seamless bi-directional communication between user equipment and radio towers, while the system's automated control mechanism ensures reliable operation even in adverse weather conditions.

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Method and device for dynamically allocating transmission resources in a wireless communication system for unmanned aerial vehicles (UAVs) and urban air mobility (UAM) vehicles. The method enables efficient resource utilization by leveraging direct communication interfaces like PC5 and sidelink, where terminals can selectively allocate resources based on specific zones and their locations. This approach optimizes resource usage while maintaining system performance, particularly in scenarios where UAVs operate in multiple zones.

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Managing flight paths for unmanned aerial vehicles (UAVs) across multiple wireless networks, enabling seamless communication between public and private networks. The system dynamically determines the network slices and subscriber profiles for each flight segment based on the UAV's planned flight path, ensuring optimal network utilization and service continuity. This approach enables UAVs to operate across multiple network domains while maintaining uninterrupted communication with their intended service providers.

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A method, system, and platform for detecting coverage edges in 5G-connected drone communication networks. The system generates quality test point data in real-time based on grid connection points, obtains network coverage quality data, and generates command data for controlling drone flight status. A detection model based on network coverage area grids is constructed to generate network coverage edge data in real-time. The system optimizes 5G network deployment by identifying coverage edges and guiding drone flight paths.

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Automated system for deploying cell-on-drone (COD) networks to mitigate traffic surges in cellular networks using machine learning and software defined networking (SDN) techniques. The system detects or predicts traffic surges in a cell site and orchestrates provisioning of resources, including COD deployment, to address the surge. It involves a central orchestrator that collaborates with a core mobility function to detect surges and coordinates safe and secure drone dispatch. The drones have SDN functionality and can intelligently deploy to offload traffic. This enables dynamic, on-demand, and centrally orchestrated COD networks for cellular network capacity expansion.

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A management apparatus enables coordinated flight of multiple drones communicating with different cellular networks by allocating zones that can be identified across multiple networks and transmitting zone allocation information to the networks. The apparatus allocates zones that can be identified across multiple networks and transmits zone allocation information to the networks. The zones are allocated to a flight airspace allowing multiple drones to fly, with each drone communicating with a different network. The zone allocation information enables coordinated flight of the drones across multiple networks.

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A smart communications hub for unmanned aircraft systems (UAS) that enables secure and efficient communication between UAS and network services. The hub uses network slicing to create virtual networks for specific UAS services, such as registration, video transmission, and IoT data collection. When a UAS broadcasts its identifier, the hub authenticates it and allocates the appropriate network slice based on the UAS's subscribed services. This enables secure and efficient communication between the UAS and network services, while also enabling network operators to manage UAS traffic and provide differentiated services.

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A cloud-edge-end cooperative control method for 5G networked UAVs in security rescue applications, which enables real-time video transmission, high-precision mapping, and emergency response through distributed computing across the cloud, edge, and UAV. The method leverages 5G network slicing to optimize data transmission latency and bandwidth, while offloading computationally intensive tasks such as SLAM and video analysis to the edge cloud. The UAV transmits image data to the edge cloud, where SLAM and mapping computations are performed, and the results are transmitted back to the UAV for navigation and control. The method also enables 4K live streaming and emergency video transmission to remote command centers through the cloud.

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Detecting and assigning network slices to unmanned aerial vehicles (UAVs) based on type, usage, QoS, and SLA metrics, while performing antenna beam management to ensure reliable connectivity during flight.

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Optimizing wireless networks for autonomous vehicle communications, particularly for Unmanned Aerial Vehicles (UAVs), to enable efficient and reliable control over wide geographical areas. The system dynamically allocates network resources to meet the unique communication requirements of UAVs, ensuring sufficient bandwidth and quality of service for real-time control and data transmission.

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Enabling unmanned aerial system (UAS) operation in cellular networks through a comprehensive set of procedures and enhancements to existing 3GPP functionalities. The solution includes network-assisted UAS discovery, direct discovery, and operation using 3GPP access technology, as well as enabling UAS communication for command and control in 5G systems with guaranteed quality of service. The solution also provides UAS identification and operation in 3GPP systems, including UAS association and identification procedures, C2 communication setup with required QoS, and flight plan-based UAS operation.

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Method and apparatus for improving cellular network service to unmanned aerial vehicles (UAVs) by enabling base stations to provide optimized network service based on UAV flight path information. The method involves a source base station obtaining UAV flight path information and transmitting it to a target base station, which then uses the information to provide network service to the UAV. The flight path information enables the target base station to predict UAV movement and provide optimized network service, improving overall service quality.

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Method for managing UAV operations in a mobile network, enabling safe and efficient flight paths through real-time network monitoring and configuration. The method integrates UAV traffic management with network optimization, using machine learning to predict and prevent collisions, maintain network connectivity, and ensure QoS compliance. The approach enables dynamic flight planning and route optimization through network-aware UAV authentication, while maintaining network security and performance standards.

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Programming unmanned aerial vehicles (UAVs) to fly between points using multiple cellular networks while avoiding switching issues. The method involves calculating a flight path that considers network coverage and switching requirements. The UAV is programmed with this path and can follow it between points. During the flight, it periodically sends reports of network connectivity. If conditions warrant, the network node can send a command to switch networks mid-flight. This allows controlled and reliable switching between cellular networks for UAVs.

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Managing UAV communications in wireless networks by dynamically allocating resources based on flight paths. The system determines the required network resources for each UAV flight and reserves them across multiple cells in a wireless network. This approach prevents interference between UAVs and other network users while maintaining reliable communication. The system achieves this by coordinating resource allocation across multiple cells based on flight paths, ensuring that resources are reserved in overlapping areas to prevent interference.

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