Network Handover for Seamless UAV Communication

Configuring unmanned aerial vehicle (UAV) services during handovers between different radio access technologies (RATs) like LTE and 5G. When a UAV moves between base stations using different RATs, the handover request contains configuration containers with UAV-specific data like identification, subscription, location, flight path, etc. This allows the new base station to continue providing UAV services without interruption. The containers can be sent before or during the handover.

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Method and device for unmanned aerial vehicle (UAV) handover, enabling fast handover based on UAV flight path. The method includes determining candidate base stations meeting handover conditions, performing handover preparation with a selected candidate base station, and directly reconfiguring the UAV's radio resource control connection when the preparation is complete. The device includes a processor configured to perform these steps.

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Enhancing cell handover efficiency in uncrewed aerial vehicles (UAVs) by dynamically preparing access resources before vehicle transition. The method involves a network element that provides UAV access services, which sends a request to prepare access resources for the next cell. The UAV receives this request and prepares the necessary resources before transitioning to the next cell. This proactive approach enables the network element to maintain continuous service continuity while minimizing handover delays.

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Communications in wireless networks during handover of unmanned aerial vehicles (UAVs) and other user equipment (UE) between base stations (BSs) that enable efficient and reliable handover operations. The system enables UAVs to communicate flight path information to BSs during handover, which facilitates proactive optimization of the handover process. This approach enables network operators to maintain continuous connectivity to UAVs while minimizing handover latency and power consumption.

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Enabling controlled and regulated flight operations for unmanned aerial vehicles (UAVs) using wireless networks. The network can plan and manage UAV routes, transition between manual and network-controlled flight modes, and override manual control in emergencies. The network can send route instructions to UAVs and take over control if needed. This allows coordinated and safe UAV operations in congested airspace.

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Programming unmanned aerial vehicles (UAVs) to fly along paths that optimize connections between mobile networks. The method involves calculating flight paths from location data that avoid coverage gaps. The paths are calculated using switching criteria to change networks mid-flight. UAVs report connection status during flight and can receive commands to adjust paths if needed. The approach provides reliable network coverage for UAVs while avoiding gaps and allowing mid-flight network switching.

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Handover optimization between serving cell equipment and special serving cell equipment dedicated to UAVs. The optimization is achieved by dynamically adjusting the transmission power levels between serving and special serving cell equipment based on the likelihood of interference between them in their overlapping coverage area. The special serving cell equipment, which typically has higher power amplifiers, is optimized to minimize intra-frequency interference when transitioning from one frequency to another, while the serving cell equipment, which typically operates at lower power levels, is optimized to reduce interference when transitioning from one frequency to another. This approach enables seamless handover between the two types of cell equipment while maintaining optimal performance in their respective coverage areas.

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Uncrewed vehicle like drones with simultaneous connectivity to multiple wireless networks to improve reliability of remote control. The vehicle has radios for concurrent connections to networks like cellular. It receives navigation instructions from a control system via one network. It also connects to a central function that monitors network quality. If the primary network degrades, the central function instructs the vehicle to switch to another network for control. This ensures critical commands are always delivered even if one network fails. The central function uses data from the vehicle like location, route, and network performance to evaluate quality.

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A method to allow a base station to obtain path information from a UAV that overcomes resource limitations. The base station first sends an indication to the UAV requesting path information. If the UAV confirms it can send the path information, the base station sends a configuration to allow the UAV to send. The UAV then sends its path information to the base station. This ensures the base station can receive the path information and avoids wasting resources if the base station cannot process it.

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Dynamic Quality of Service (QoS) management for communication between unmanned aerial vehicles (UAVs) and ground control stations (GCS) through a unified network architecture. The system enables efficient QoS provisioning across different communication paths (uplink and downlink) between UAVs and GCSs, ensuring optimal performance across varying network conditions. The QoS management is achieved through a group-based approach that dynamically adapts to communication requirements between UAVs and GCSs, enabling seamless QoS management across different communication paths.

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Evaluating cells of a communication network to identify cells suitable as handover targets for communications with an airborne vehicle. The evaluation involves detecting an unmanned aerial vehicle (UAV) airborne with respect to a terrestrial communication network, obtaining first geolocation information for the UAV, obtaining second geolocation information for a cell of the terrestrial communication network detected by the UAV, calculating an expected receive (RX) power for a signal transmitted from the UAV to the cell, comparing the expected RX power with a threshold power, and determining, based at least in part on the comparing, whether the cell is a suitable target cell for a communications handover procedure for the UAV.

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Cloud-based drone management system that optimizes UAV operations through real-time network monitoring and intelligent control. The system retrieves network state information and determines operational constraints, then uses this data to compute optimal flight paths and resource allocation for UAVs. This closed-loop feedback loop enables real-time optimization of UAV performance in diverse network conditions, while maintaining reliable communication services.

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Systems, methods, and instrumentalities for cellular connectivity and quality of service (QoS) monitoring and prediction for unmanned aerial vehicle (UAV) communication. The system enables core network selection of assisting WTRUs for communication link monitoring based on a target UAV's flight route, and utilizes monitoring reports from assisting WTRUs or adjacent pilot UAVs to predict communication link quality for the target UAV's flight path.

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Communications in wireless networks during handover of unmanned aerial vehicles (UAVs) and other user equipment (UE) between base stations. The system enables seamless handover by dynamically transmitting flight path information between base stations, enabling efficient resource optimization and maintaining connectivity. The information includes detailed flight trajectory data, enabling proactive optimization of handover processes.

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Self-organizing, autonomous network of unmanned aerial vehicles (UAVs) that can provide high-bandwidth wireless backhaul connectivity over long distances beyond line-of-sight. The network uses a high bandwidth mmWave wireless mesh backhaul between the UAVs to enable applications like LTE coverage in disaster areas, wide-area search and rescue, and autonomous surveillance in inaccessible areas. The UAVs jointly optimize position, yaw, and traffic routing to efficiently configure the network. A migration process determines the optimal configuration in the least time to reconfigure the network dynamically in response to events.

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Replacing a first drone base station with a second drone base station in a wireless cellular network, without user terminal handover, to provide seamless transition for connected devices. The key aspects are: 1. Having the replacement drone use the same radio frequency band and cell identifier as the first drone, 2. The first drone stops service and flies away once the replacement drone arrives. This allows the replacement drone to take over the cell coverage without any visible change to user devices, as they maintain the same network cell ID and radio connection.

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An unmanned aerial vehicle (UAV) control system and method that dynamically adjusts reporting intervals based on historical and current status information to optimize communication quality, power consumption, and data transmission efficiency. The system calculates variance between historical and current status data and updates the reporting configuration accordingly, enabling adaptive reporting that balances communication reliability with power efficiency.

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Base station handover method that allows for continuous service of an unmanned aerial vehicle and broadens an application range of the unmanned aerial vehicles. The method includes determining that the trigger condition for handover of the base station is met, and determining the target base station as a target base station for which the unmanned aerial vehicle requests handover.

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Self-forming, self-organizing, cooperative, autonomous RF communication network using UAVs that can form and optimize their own relay links without central coordination. The UAVs cooperatively choose their positions and RF configurations based on game theory algorithms to maximize network availability and reliability. An oversight controller updates the UAVs' decision criteria. The UAVs use opportunistic array theory to determine optimal relay configurations. The cooperative reasoning engine coordinates the UAVs to balance network performance and individual goals.

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A method for wireless communication between an unmanned aerial vehicle (UAV) and a network node, enabling flexible flight path reporting. The method involves the network node transmitting a format indicator to the UAV, specifying the desired format for flight path information. The UAV then transmits the flight path information in the requested format, eliminating the need for the UAV to reencode the information. This approach reduces computing resources expended by both the UAV and the network node, particularly when the network node can directly utilize the requested format.

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