Energy Efficiency Optimization in Long-Range Drone Systems

Energy efficient method for an unmanned aerial vehicle (UAV) to wake up and communicate with sensor nodes in a wireless network where the nodes can't be powered by wires or batteries. The method involves optimizing the UAV's flight path and the sensor nodes' wakeup times to minimize total energy consumption. It splits the communication process into two stages: setup and data transfer. By alternately optimizing the node schedules and UAV path, it finds a suboptimal solution to the mixed integer nonconvex optimization problem.

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Optimizing resource allocation for unmanned aerial vehicles (UAVs) in wireless power supply networks to improve system efficiency and prevent data loss. The method jointly optimizes UAV flight position, wireless energy transmission time, and wireless information transmission time for multiple ground nodes. It maximizes the sum of effective capacities, which represents the maximum constant arrival rate of data that can be supported with guaranteed quality of service. This reduces delay and prevents backlogging in node buffers. By considering both energy and information transmission, it improves overall resource utilization compared to separating them.

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Energy efficiency optimization method for unmanned aerial vehicle (UAV) assisted backscatter communication systems. The method involves finding the optimal UAV flight path and altitude to balance throughput and energy efficiency. It models the UAV-BN communication system, calculates energy consumption and throughput at different heights, and finds the height and path that maximizes the energy efficiency metric. This addresses the tradeoff between UAV coverage and energy efficiency in UAV-assisted backscatter communication systems.

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Energy efficiency optimization method for unmanned aerial vehicle (UAV) assisted wireless power supply networks to improve overall system efficiency by jointly optimizing wireless power and data transmission parameters. The optimization involves finding the UAV transmitting power, node powers, and transmission times that maximize system energy efficiency while meeting service quality constraints. This improves UAV-assisted wireless power supply networks compared to ignoring circuit power consumption in UAV-assisted sensor networks. The optimization balances uplink throughput and total energy consumption to reduce overheads.

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Downlink power control for unmanned aerial vehicle (UAV) data links that improves efficiency and range by adjusting transmission power based on UAV distance. The power control method involves the UAV and ground station exchanging distance measurements over the uplink. The ground station provides the UAV with a lookup table of transmit powers based on distance segments. The UAV then uses the segmented table to automatically adjust its downlink power as it flies. This reduces power consumption compared to fixed power or open loop methods while maintaining reliable communication over long distances.

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Low-power consumption optimization method for unmanned aerial vehicle (UAV) assisted backscatter edge computing networks. The method involves using UAVs to provide energy and computation offloading for devices with limited power and shielding between them and base stations. The UAVs transmit RF signals to power the devices, which communicate with the UAVs using backscatter. Devices offload computationally intensive tasks to the UAVs, which return results. This allows devices to conserve power compared to active transmission. Devices store remaining UAV-provided energy for later use. The UAV trajectory is optimized to balance energy consumption vs QoS.

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Method for optimizing energy utilization and network coverage in unmanned aerial vehicle (UAV) assisted wireless sensor networks. The method involves two phases: node registration and energy carrying communication. In the registration phase, sensor nodes inform the UAV of their energy levels. In the communication phase, the UAV hovers over nodes with low energy to charge them. The UAV balances charging time vs flight distance to maximize coverage. It also maintains a minimum received power threshold to ensure efficient charging. By registering node energy and balancing charging vs flight, the UAV can optimize energy utilization and distribute charge evenly among nodes.

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Energy-efficient unmanned aerial vehicle (UAV) relay cooperative communication system for expanding network coverage with optimal power consumption. The method involves UAV relays collecting energy from previous relays instead of batteries. It optimizes relay node placement, signal coding, and power allocation to maximize information rate while minimizing energy consumption. The UAV relays cooperatively transmit data by relaying signals through multiple hops. The method uses signal-to-noise ratio approximations to convert the optimization problem into convex form for efficient solution.

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Method and apparatus for controlling transmission power of unmanned vehicles in a wireless communication system to balance link reliability and interference mitigation. It involves checking the margin and required power of a non-mission data link, comparing it to the maximum power, and determining the optimal transmission power based on the margin. This prevents overpowering that causes interference while ensuring adequate link quality. The ground station can also send power control commands to adjust the unmanned vehicle's transmit power.

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Energy collection method for unmanned aerial vehicle (UAV) edge computing systems using simultaneous wireless information and power transfer (SWIPT) to extend UAV endurance. The method involves optimizing energy efficiency of the UAV swarm by coordinating power allocation between base station and relays. Steps include: building a UAV swarm working model, calculating energy consumption and efficiency, determining optimal power allocation between base station and relays, and implementing the allocation in the swarm. This balances energy between base station and relays to maximize overall system endurance.

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Maximizing energy efficiency in a drone-assisted D2D communication network by optimally allocating resources like spectrum and power. It considers uncertainties like channel gains and user positions, converts the optimization problem into a convex form, and uses a Lagrangian dual theory and sub-gradient algorithm to find an analytic solution. This improves robustness and efficiency compared to other algorithms as it accounts for factors like distance and channel quality.

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Communication method and system for vertical take-off and landing fixed-wing drones to reduce power consumption and improve flight duration. The method involves calculating the real-time signal strength between the drone and control device based on distance and power. If the signal strength is below a threshold, a power enhancement signal is sent to the drone to increase communication power. This prevents the drone from always using high power for communication, reducing consumption and extending flight time.

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Millimeter wave network communication method for unmanned aerial vehicle (UAV) assisted wireless power supply that improves efficiency and reliability of emergency communication systems using UAVs as flying base stations. The method dynamically adjusts parameters like beamforming and response time slots based on user distribution to optimize performance and battery life. It also calculates communication periods, charge ratios, and throughputs to find the best UAV flight paths. The method aims to maximize user data rates, extend battery life, and dynamically match beamforming to user density as UAVs fly over unknown disaster areas.

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Controlling the communication resources allocated to user terminals by a base station on a high altitude platform station (HAPS) drone based on the remaining battery capacity. This prevents overloading the drone's battery when communication demand exceeds solar charging capacity. The HAPS drone's control device acquires the battery capacity and instructs the base station to reduce communication resources allocation as the battery level decreases.

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Communication system for improving the endurance of unmanned aerial vehicles (UAVs) by dynamically adjusting the UAV's communication power based on real-time signal strength. The system receives UAV positioning data and power levels. It calculates the UAV's distance and signal intensity using trigonometry. If the signal strength is below a threshold, it sends a power increase command to the UAV to boost communication range. This prevents UAVs from wasting power on excessive communication when signals are weak.

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Optimizing flight time and energy efficiency of drones used as mobile base stations for wireless communication and energy transfer to ground sensors. The optimization involves finding the drone's optimal flight speed and schedule for energy transfer and data collection to minimize flight time while meeting communication requirements. The drone provides wireless power to sensors via RF energy harvesting, allowing sensors to operate without batteries. The optimization algorithm iteratively computes energy transmission and data collection times for each sensor.

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Efficiently managing energy use in drone networks to enable longer mission durations. The drones in a network coordinate signal power levels to balance consumption and acquisition. When a drone sends a signal to another drone, it reduces the power to the minimum needed for reception. The receiving drone sends a return signal with the required minimum power level. This allows the original drone to modulate its signal power down to that level. The intermediate drone now receives the lower power signal with excess power. It harvests that excess power using an energy collection subsystem. This balances power consumption and acquisition between drones.

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A method and device for optimizing communication performance of passive nodes in an unmanned aerial vehicle (UAV) wireless energy supply system. The method involves calculating the UAV's energy transmission power to each node based on their collected energy, then calculating each node's transmission power for data based on the energy received. This balances energy and data transfer for efficient UAV-enabled wireless power harvesting networks.

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Optimizing power consumption and reducing electromagnetic radiation of drones and remote controls by dynamically adjusting signal transmission power based on the distance between the drone and remote control. It reduces power waste and interference when flying close by compared to maintaining maximum power for long range flights.

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An unmanned aerial vehicle (UAV) communication method and system that enables long-range UAV flights by intelligently managing communication resources. The method involves predicting UAV flight distance, and dynamically adjusting communication parameters like frequency and power based on that prediction. This optimizes communication efficiency for the specific flight distance. The system also includes heartbeat packets sent periodically to detect if the primary communication path is working, allowing backup paths to be used if necessary.

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