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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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 and device for reporting flight mode changes in unmanned aerial vehicles (UAVs) operating in cellular networks. When a UAV transitions from a fixed mode to a dynamic mode while connected to a base station, it sends a flight mode change notification to the base station. The base station receives the notification, determines the mode change, and adjusts its control strategy accordingly to ensure optimal network management and UAV operation.

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Method for a base station to obtain path information from a UAV, comprising: receiving indication information from the UAV indicating its capability to send path information; sending configuration information to the UAV indicating whether it is allowed to send path information; and receiving path information from the UAV when allowed. The method enables a base station to determine whether a UAV can send its path information and receive it when authorized.

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Method for dynamically adjusting network settings based on UAV flight zones. The method enables UAVs to automatically adapt their network configuration to their specific flight environment by determining the appropriate network zones based on their altitude and height thresholds. The UAVs receive network settings that correspond to their current flight zone, enabling them to optimize their communication and data transmission capabilities. This approach enables UAVs to efficiently manage network resources while maintaining reliable communication with ground stations.

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Aerial navigation system for drones and other aerial vehicles that enables efficient sharing of airspace and network resources while flying. The system uses flight plans coordinated based on starting and destination points, air traffic conditions, and network coverage. The flight plans include preferred base stations along the route to connect to and drop signals from others. This reduces collisions, maintains connections, and meets app quality requirements. Drones can collect air traffic data en route to update plans.

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Managing Unmanned Aerial Vehicles (UAVs) in a 3GPP network during cell handovers based on a flight plan. The system enables seamless handovers between cells in the cellular network by utilizing UAV measurements and flight plan data to trigger handovers when the UAV approaches a cell boundary. This ensures continuous connectivity and reliable communication between the UAV and the network during flight.

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Managing UAV handovers between cells in a wireless network using flight plans from a UAV Traffic Management (UTM) system. The system detects handover conditions, determines target cells based on flight paths, and initiates handovers between source and target cells. It preserves network session state during out-of-coverage transitions, enables automated alarms for missing UAVs, and optimizes handover procedures through predictive routing and caching.

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A wireless communication method that improves handover performance between a drone and a network device by enabling the network device to predict the drone's future flight path based on reported location information. The method involves the drone reporting its flight path to the network device, which uses this information to determine the optimal cell and network device for handover. This enables proactive handover planning and reduces the likelihood of handover failures.

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System for detecting connectivity anomalies in wireless networks to ensure safe operation of unmanned aerial vehicles (UAVs) beyond visual line of sight (BVLOS). The system continuously monitors network coverage in a 3D flight area and compares it to predicted coverage data used for UAV guidance. When a deviation is detected, the system identifies the location and type of anomaly, determines an area of risk, and reports the information to aviation control nodes for safe UAV operation.

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Updating unmanned aerial vehicle (UAV) status in unmanned aircraft system (UAS) ecosystems to improve efficiency and safety. The UAV's flight state is communicated to the UAS traffic management (UTM) using the cellular network. The UAV's user equipment (UE) determines the flight state based on factors like altitude, speed, and drone components. By providing UTM real-time flight state information, it enables optimized path assignment, power management, and reduced latency compared to just knowing if the UAV is at the source/destination.

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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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A wireless communication system for drones that enables efficient handover between base stations during vertical flight. The system includes a base station that transmits reference signals and notifies drones of altitude zone settings, and a drone that measures reference signal strength and altitude, and reports measurement results to the base station based on the altitude zone settings. The system controls measurement report timing and frequency based on drone speed and altitude to ensure seamless handover between base stations during vertical flight.

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Enhanced handover support for aerial user equipment (UE) in cellular networks, particularly for drones, through base station/network-initiated and UE-initiated mobility enhancements. The enhancements leverage predictable drone channel properties, such as slower signal attenuation and elevation nulls, to optimize handover procedures and reduce ping-pong effects. The system includes new signaling mechanisms for both base station/network-initiated and UE-initiated handover enhancements, enabling more efficient and reliable handover management for aerial UEs.

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Determining the quality of mobile networks in air corridors for autonomous drone operations. The method employs an unmanned aerial vehicle (UAV) equipped with a mobile communications receiver and positioning device to continuously monitor network performance across multiple heights. The receiver measures network quality at each position, while the positioning device maintains the UAV's position. The data is transmitted to a ground station for analysis, enabling real-time optimization of network performance and flight planning. The method ensures continuous network connectivity throughout the flight path, enabling autonomous drone operations in previously untested air corridors.

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A communication method, equipment, and system for drones that improves handover success rates by enabling access network devices to prepare resources for incoming drones before they switch cells. The method involves the access and mobility management network element obtaining the next hop access information of the drone and sending it to the current access network device, which then requests the next hop access network device to prepare resources. The next hop access network device responds with cell information, allowing the drone to select and access the optimal cell.

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Unmanned aerial vehicle (UAV) network cell that enables wireless communication coverage through modular communication hardware components. The cell features modular communication modems that can be installed and removed from a system board, allowing seamless upgrades to support different wireless standards and frequency bands. The cell integrates multiple communication frequencies and protocols, enabling continuous communication with the core network while supporting network expansion through hardware upgrades.

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Optimizing UAV network performance through strategic positioning decisions using game theory. The system employs a dynamic, multi-UAV approach where each UAV alternates between optimal and random positioning strategies to achieve network-wide reliability and coverage. The decision-making process incorporates reward matrices that evaluate the trade-offs between network reliability and coverage. By iteratively adjusting UAV positions based on these evaluations, the system converges towards a Nash Equilibrium that balances network reliability and coverage while minimizing latency and energy consumption.

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Method and apparatus for unmanned aerial vehicle (UAV) recovery operations when radio connection is lost. The method includes detecting radio connection quality degradation at the UAV, autonomously directing the UAV to new geographical coordinates to maintain or reestablish the connection, and broadcasting a connection request beacon to other UAVs in the area for forwarding to the network. The apparatus includes a processor and memory configured to perform these operations.

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Mitigating interference in 5G NR systems by dynamically switching between omnidirectional and directional transmission modes based on altitude and cell density. When an unmanned aerial vehicle (UAV) operates at elevated altitudes or detects multiple cells, the system switches to directional transmission mode, utilizing narrower beams to minimize interference with neighboring cells. This approach enables efficient interference mitigation while maintaining optimal coverage for UAV operations.

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Using UAV-mounted base stations to provide continuous cellular connectivity while moving and use machine learning to accurately localize ground users in real time. The UAV-mounted base stations can retrieve user device information and their own locations over time to determine user positions without needing additional base stations.

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