Power Management for Drone Communication Systems

A method for optimizing federated learning performance and resource allocation using unmanned aerial vehicles (UAVs). The method determines optimal UAV positions, user resource allocation, and learning parameters to balance energy consumption and learning performance. It models the problem as an optimization problem that minimizes a total cost function representing energy consumption and learning performance, and solves it to obtain optimal UAV positions, user resource allocation, and learning parameters.

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Uplink/downlink resource allocation, beam adjustment, and power control for unmanned aerial vehicle (UAV) communication, enabling efficient resource management in high-flying, high-speed environments. The system establishes a mapping relationship between three-dimensional spatial regions and resources, allowing base stations to dynamically allocate resources based on UAV location and channel conditions. The mapping relationship is initially established through prior knowledge of resource allocation patterns, enabling real-time resource determination for UAVs. The system also implements optimized uplink power control parameters, enabling faster and more effective power adjustment in UAV communication systems.

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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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Wireless communication method for unmanned aerial vehicles (UAVs) that enables grouping information exchange between wireless communication nodes. The method includes transmitting UAV grouping information, such as group identifiers or authorized information, between nodes, and receiving UAV-related information, including identifiers, differentiation data, and flight parameters. The method enables coordination of multiple UAVs in a wireless network, facilitating applications like relaying and monitoring.

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Efficient resource allocation and uplink power control for unmanned aerial vehicle (UAV) communication systems. The system enables real-time mapping of UAV spatial locations to resources through pre-established channel quality relationships, allowing base stations to dynamically allocate resources based on the UAV's current spatial context. This mapping enables efficient resource allocation and real-time optimization of uplink power levels, particularly in high-flying UAVs with variable channel conditions.

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Ensuring Quality of Service (QoS) for communication between unmanned aerial vehicles (UAVs) and their ground controllers through a unified network architecture. The system dynamically adapts QoS parameters based on the specific communication requirements between UAVs and ground stations, enabling optimized performance across different communication modes (direct, network-assisted, and UTM-navigated). The architecture achieves this through a unified group-based QoS provisioning approach that automatically adjusts parameters based on the specific communication scenario.

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A wireless perception system energy and information transmission method for a UAV swarm, enabling autonomous coordination of multiple UAVs to supply power and transmit data to a network of wireless sensors. The method employs a multi-AP architecture, where each UAV serves as an energy transmitter and data receiver for a subset of sensors, and optimizes transmission power, beamforming weights, and time division factors to balance energy harvesting and data throughput across the swarm.

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Optimizing UAV data acquisition systems through coordinated flight trajectory, wake-up scheduling, and time slot management to achieve maximum energy efficiency. The method iteratively optimizes these parameters through a combination of sub-problems, ensuring that both the transmission data amount and energy consumption of sensors are optimized while maintaining system performance.

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A system and method for controlling Quality of Service (QoS) in wireless links using multiple relay stations, including mobile stations such as drones. The system continuously monitors QoS metrics across the link and identifies hops where target values are not being met. It then determines and executes corrective actions to improve QoS, including adding mobile relay stations as needed to maintain sufficient quality.

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Real-time switching of power sources in unmanned aerial vehicles (UAVs) based on load profiles to optimize performance and battery life. The system monitors flight phases and commands to proactively switch between lithium polymer (LiPo) and lithium ion (Li-Ion) batteries in real time as the load profile changes. This allows using the appropriate battery type for the current load conditions to maximize efficiency and avoid battery degradation.

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Powering unmanned aerial vehicles (UAVs) through a novel battery management system that optimizes energy harvesting and storage during solar-powered flight. The system enables the UAV to ascend to higher altitudes during daylight hours by utilizing excess energy generated by the solar array, while maintaining optimal battery temperature limits. This approach enables the UAV to conserve energy during nighttime operations by descending to lower altitudes after sunset, thereby extending its flight duration and reducing overall mission duration. The system incorporates a power management system that dynamically controls charging and discharging of the battery pack based on solar array output and flight conditions.

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Enabling QoS management for UAV-Controller communication through a unified approach that integrates multiple network technologies. The system provisions QoS parameters for direct UAV-Controller communication, enabling both UAV originated and terminated QoS configurations. The provisioning is achieved through a group-based approach that recognizes UAV-Controller pairs based on their unique identifiers, allowing for centralized management of QoS parameters across both UAV and controller. The system enables adaptive QoS adaptation when communication conditions fail, ensuring consistent network resource allocation for both UAV and controller operations.

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Method for cell reselection in UAVs that avoids frequent cell reselection and improves stability while reducing power consumption by dynamically adjust the reselection parameters based on altitude. The base station sends the UAV a measurement parameter adjustment rule including altitudes and corresponding offsets/factors. The UAV adjusts its reselection parameters based on its current altitude using the rule. This avoids constant reselection as altitude changes, improving stability.

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Wireless communication system for unmanned aerial vehicles (UAVs) that optimizes resource allocation, beam adjustment, and power control through a mapping-based approach. The system establishes a mapping relationship between three-dimensional spatial regions and resources, where the base station allocates resources based on the mapping. The mapping is determined based on channel quality measurements and spatial location information, enabling real-time resource allocation and measurement. The system also provides fine-grained uplink power control parameters, enabling faster power adjustments compared to conventional configurations. This mapping-based approach enables efficient resource management for UAVs operating in environments with varying channel conditions.

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Direct command and control communication between a UAV and a controller through a unified network architecture, enabling optimized QoS management for both uplink and downlink communications. The system leverages a group-based approach to manage QoS across both the UAV and controller, with separate groups for uplink and downlink communications. A centralized network resource manager coordinates QoS provisioning and adaptation based on group identifiers, ensuring consistent QoS levels across both communication paths. This unified approach enables efficient QoS management while maintaining separate control interfaces for the UAV and controller.

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Optimizing the movement of multiple UAVs to efficiently provide wireless coverage with energy conservation. It uses a deep learning model called DDPG to continuously control UAV movements based on real-time observations. The model generates continuous control signals instead of discrete ones like Q-learning. The observations include UAV energy, user coverage, and fairness. The model learns from sampled mappings between observations and control signals. This allows more precise and fluid UAV movement control compared to discrete methods.

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Method for cellular networks to automatically detect when a drone-coupled wireless device like a UAV drone takes off and apply specialized protocols to optimize its connection. When a drone takes off, it triggers a session connectivity request to the network that includes an identifier for the aerial session. The network establishes a dedicated bearer with the drone and applies aerial-specific protocols for things like power control, handover, antenna config, etc. This allows the drone to maintain optimized connectivity while flying without interfering with ground users.

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A system and method for controlling Quality of Service (QoS) in wireless links using multiple relay stations, including mobile stations like drones. The system continuously monitors QoS metrics and determines when target values are not being met. It then identifies and executes corrective actions to maintain QoS, including adding mobile relay stations as needed to ensure reliable communication.

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Wireless communication system enabling status reporting for airborne devices. The system includes a wireless device that reports its flight status to the network using a two-bit field with four distinct states, each corresponding to a predefined status. The network receives the status indication and configures the device accordingly, including power control, radio resource allocation, and handover management. The system also enables the network to detect and mitigate interference caused by airborne devices, and to optimize network performance based on the device's flight status.

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A system for aircraft monitoring that enables real-time data collection during flight phases while minimizing power consumption. The system comprises a sensor, a radio module conforming to a low-power wide area network standard, and a power management module with energy storage. The radio module transmits data to a radio access point, where it determines transmission power based on the aircraft's power state. The system implements adaptive transmission power management to optimize power consumption while maintaining data transmission capabilities.

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