Spectrum Management for Drone Operations

A wireless communication method for unmanned aerial vehicles (UAVs) that enables reliable mission data transmission in areas where main cellular networks are unavailable. The method involves using a customized radio frame structure with overlapping uplink and downlink slots. This allows simultaneous UAV uplink and downlink transmissions within a frame. A short guard interval separates the slots to mitigate interference. This enables UAVs to transmit both control and mission data using the same radio frame without conflicts, facilitating stable operation in areas outside cellular coverage.

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Managing operations of unmanned aerial vehicles (UAVs) that utilize terrestrial communication systems. The system retrieves network state information describing the network conditions and determines the impact on UAV operations. It then provides operational information to the UAV based on the network state to optimize performance and reliability.

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Optimizing wireless communications for unmanned aerial vehicles (UAVs) by dynamically selecting the carrier frequency based on network conditions and UAV operations. The method involves retrieving network state information describing the network conditions, determining the impact of those conditions on the UAV's operation, and then selecting an appropriate carrier frequency for UAV-network communications based on that impact. Factors like altitude, location, bandwidth availability, and future flight paths are considered.

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Optimizing uplink power control for unmanned aerial vehicles (UAVs) when they fly near cell towers. When a cell tower adjusts its transmit power, it sends a paging message to UAVs to update their uplink power settings. This prevents UAVs from wasting battery by unnecessarily increasing uplink power when the tower's coverage expands. The tower initiates the paging when it changes transmit power, rather than waiting for UAVs to reread the cell ID.

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System for improving reliability and scalability of beyond visual line of sight unmanned aerial vehicle (UAV) communication links by dynamically routing data between UAVs and ground stations. The system uses a network of UAV radios and ground stations all tuned to the same frequency. The ground stations receive multiple parallel channels on that frequency using techniques like GMSK modulation. An OSPF routing algorithm selects the best ground station for each UAV based on link performance parameters. This allows seamless handoff between ground stations as the UAV moves and optimizes connectivity for multiple UAVs.

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Method for controlling communication between unmanned aerial vehicles (UAVs) and their controllers using a wireless network to enable authorized UAV-UAV and UAV-controller communication while preventing unauthorized flights. The method involves registering UAVs and controllers for UAS services with a central traffic management system. When a UAV requests a network session, the network checks if the destination UAV/controller is allowed through the management system. If so, it sets up the session. This ensures UAVs can only communicate with authorized targets.

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Spectrum allocation optimization for covert transmission of multi-drone networks to improve spectrum utilization while ensuring confidentiality. The method involves maximizing drone-to-ground information transmission satisfaction subject to routing, bandwidth, power, block length, concealment, and SNR constraints. It transforms multi-drone routing into an alliance game, jointly optimizes routing, bandwidth, block length, and power using iterative coordinated descent and particle swarm optimization.

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Determining dynamic frequency strategies for electromagnetic targets based on spectrum monitoring data to optimize spectrum utilization and mitigate interference. The method involves prioritizing high-priority targets first, then allocating idle spectrum, followed by spatial multiplexing and time domain staggering for lower priority targets. This iterative process is repeated with each time period to adapt the frequency strategy as spectrum usage changes.

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Managing communication connections of unmanned aerial vehicles (UAVs) in a mobile network to enable reliable and efficient UAV operations while addressing challenges like ping-ponging, interference, and coverage. The method involves receiving UAV requirements, determining optimal radio frequency bands based on factors like flight path, weather, restrictions, and task needs, and sending a control signal to the UAV with the selected bands. This allows the UAV to use the specific bands during flight to improve performance, coverage, and reliability.

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Spectrum resource allocation and power collaborative regulation method for unmanned aerial vehicle (UAV) computing networks to improve spectrum efficiency and reduce total energy consumption in UAV computing networks. The method involves using non-orthogonal multiple access (NOMA) multiplexing channels for UAVs and ground terminals, cluster division to group users, and optimizing transmit power using the Lagrange multiplier method.

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Intelligent spectrum sensing and anti-interference communication system for self-organizing network drones that enables adaptive frequency selection and dynamic spectrum access for drones operating in dynamic and interference-prone environments. The system uses onboard spectrum sensing to detect interference levels at candidate frequencies and dynamically select the best frequency point for communication based on interference. This allows drones to avoid strong interference frequencies and use complex modulation techniques to improve reliability in poor conditions. The system also periodically checks eliminated frequencies to restore them if interference clears.

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Maintaining network connectivity for unmanned aerial vehicles (UAVs) during flight to facilitate aerial navigation and operation. Flight routes and channel allocation instructions are generated based on flight plans and geographic information. This allows UAVs to connect to cellular networks at altitude without interference. The instructions can be adjusted during flight to mitigate issues like interference events. The coordinated airspace sharing and network distribution improves efficiency and safety of UAV operations.

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Optimizing RF spectrum allocation for air-to-ground communications in aviation networks to enable reliable and continuous communications for flights. The method involves generating RF coverage plans for flights based on submitted flight plans. The plans allocate specific spectrum resources at each location during the flight to avoid interference and ensure coverage. The system considers factors like geographic ranges, altitudes, and other flights. It can also optimize spectrum usage globally across multiple flights. This allows coordinated spectrum assignments for concurrent flights without degrading link quality.

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Frequency optimization allocation method based on spectrum utility for efficient and flexible allocation of limited spectrum resources in joint operations. The method involves maximizing a spectrum utility function that considers factors like frequency requirements, efficiency, and priority. This allows rational and efficient spectrum usage while supporting strategies like capacity priority, mission priority, and frequency efficiency priority. It improves spectrum efficiency, flexibility, and adaptability compared to traditional methods. The utility function takes spectrum usage as the independent variable and compares benefits across users/equipment.

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Improving wireless communication between drones and ground stations in congested RF environments by having the drones act as access points and isolating uplink traffic to a single resource unit while allowing wider resource units for downlink. This reduces interference and improves reliability compared to channel switching. The drone connects to the ground station using a wireless protocol that divides spectrum into resource units. The drone identifies a single resource unit for uplink and instructs the ground station to transmit there. This isolates uplink traffic and prevents interference from other devices. The downlink uses wider resource units.

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Optimizing communications between unmanned aerial vehicles (UAVs) and terrestrial networks by dynamically selecting carrier frequencies based on network state information and UAV operational constraints. This allows matching UAV connectivity needs with available network resources. The system retrieves network state data describing network conditions and UAV impacts, then selects frequencies to minimize impact on UAV operations. Factors considered can include altitude, location, network load, future requirements, current conditions, etc.

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Managing RF spectrum for air-to-ground communication in aviation to provide reliable and continuous links for unmanned drones and other aircraft. A flight plan-based system allocates dedicated spectrum channels to aircraft during flights. It determines available spectrum based on flight details, selects channels, and avoids interference. The system also dynamically configures links during flights to mitigate issues. This allows aircraft to have uninterrupted comms across multiple bases without contention.

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Joint optimization of multi-UAV deployment and spectrum allocation for drone-assisted ground communications to improve data rates and spectrum efficiency. The optimization decomposes into sub-problems of UAV deployment, UAV-ground user correlation, and spectrum allocation. It uses algorithms like particle swarm for UAV deployment, local iterative optimization for user correlation, and interference-aware channel reuse for spectrum. By iteratively solving these sub-problems, it finds optimal UAV locations, associations, and spectrum assignments that minimize the number of UAVs needed to serve ground users while maximizing their data rates.

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Method for managing spectrum resources in unmanned aerial vehicle (UAV) swarms operating in dense environments like urban areas. The method involves a distributed iterative algorithm for efficient and stable frequency spectrum sharing in scenarios like UAV swarms, heterogeneous cellular networks, and dynamic networks. It solves the channel scheduling and power allocation problems separately while fixing one variable. The channel scheduling is an unconstrained optimization and the power allocation is a constrained convex optimization. This allows finding the optimal solution for each variable while fixing the other. The method enables efficient spectrum sharing in complex network scenarios with limited resources.

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Efficient dynamic spectrum sharing for swarms of unmanned aerial vehicles (UAVs) to enable coordinated spectrum usage in multi-UAV networks. The method involves a hierarchical spectrum sharing strategy that improves spectrum efficiency and reduces interference compared to individual UAVs dynamically choosing frequencies. The strategy involves a central coordinator that plans and coordinates spectrum usage across the swarm based on task requirements and UAV locations. The UAVs follow the coordinated spectrum assignments instead of independent frequency selection. This provides coordinated spectrum usage and reduces interference compared to individual UAVs dynamically choosing frequencies.

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Energy-efficient cooperative spectrum sensing for unmanned aerial vehicles (UAVs) in cognitive radio networks. The method aims to reduce energy consumption while maintaining detection performance in a scenario with multiple UAVs sensing frequency bands. It optimizes parameters like fusion threshold and sensing time to balance energy efficiency and detection probability. The UAVs sense frequency bands, report results to a central UAV, which averages and normalizes to judge idle bands. This allows UAVs to access those bands. The optimization balances energy savings vs. false alarm reduction.

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Method for allocating electromagnetic spectrum in swarm drone networks to efficiently utilize spectrum resources, reduce interference, and improve anti-jamming capability. The method involves three steps: frequency allocation, time distribution, and power control. Frequency allocation is hierarchical to save resources. Time division multiplexing is used to further reduce interference. Power control is non-cooperative to optimize performance. The method is based on game theory algorithms.

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Dynamic frequency allocation and management for unmanned aerial vehicles (UAVs) to increase spectrum utilization and allow multiple UAVs to share limited control frequencies. The method involves analyzing interference between channels and dynamically assigning and reusing frequencies in real time during UAV operation. This allows efficient frequency reuse and channel sharing compared to fixed assignments. After UAV operation, frequencies are recovered for other uses.

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A spectrum access method for unmanned aerial vehicles (UAVs) to efficiently allocate spectrum resources and improve throughput in dynamic wireless environments. The method uses a multi-user non-coupling queuing algorithm to balance channel selection, stability, and throughput. The UAVs make independent channel decisions based on queue sizes, interference power, and historical usage. This allows distributed UAV swarms to coordinate spectrum access without centralized control. The algorithm reduces frequency conflicts, improves channel utilization, and maintains stable queues compared to centralized or coupled methods.

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A cellular-based communication system for unmanned aerial vehicles (UAVs) and remotely piloted vehicles (RPVs) that enables reliable and high bandwidth communication for commercial applications while complying with airspace regulations. The system uses two separate RF frequency bands: one for payload data transmission between UAVs and ground stations, and another dedicated band for critical command and control datagrams between UAVs and airspace controllers. This allows separate communication channels for UAV functions like sensing vs navigation. It also provides elevated communication areas for UAVs to communicate via cellular-like networks above ground level. This allows UAVs to continue communicating with ground stations while operating in controlled airspace.

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System to enable reliable communication with unmanned aerial vehicles (UAVs) and remotely piloted vehicles (RPVs) operating at different altitudes. The system uses separate frequency bands for ground-level and aerial communications. It projects cellular network coverage into the sky to provide continuous communication for UAVs and RPVs at altitude. The ground-level band is for terrestrial devices. The aerial band is for aircraft using skyward-pointing antennas. Separation by frequency and polarization prevents interference. This allows reliable command/control at altitude without satellite dependence.

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Cognitive unmanned aerial vehicle (UAV) communication network design method using intelligent reflectors to improve network performance and spectrum efficiency. The method involves optimizing UAV position, phase shifts on intelligent reflectors, and sensing durations. It leverages cognitive radio to enable UAVs to sense primary network states and dynamically access them. This coordinated optimization of UAVs, reflectors, and spectrum sensing mitigates interference, reduces waste, and maximizes reachable rates.

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Spectrum allocation method, electronic device and storage medium for frequency usage behavior that improves spectrum efficiency and reduces interference by considering frequency requirements and compatibility of devices' frequency usage stages. It allocates frequencies sequentially based on stage priorities. Devices input their frequency usage stages, equipment, spacing, etc. An interference matrix is calculated for each stage. Frequencies are assigned by stage priority to avoid conflicts.

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Authorization-free spectrum access method for unmanned aerial vehicles (UAVs) in a mobile edge computing (MEC) network using unlicensed spectrum. The method involves joint optimization of duty cycle, flight path, power allocation, and bandwidth assignment to maximize total capacity of UAV-based MEC. It allows UAVs to serve multiple user types with different requirements in hotspots using unlicensed spectrum. The optimization balances UAV mobility, spectrum sharing, and user rate guarantees.

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Spectrum sharing method for satellite-UAV hybrid networks that allows efficient sharing of spectrum between satellites and UAVs. The method involves optimizing UAV power allocation and hover times to balance UAV communication needs with satellite interference constraints. By maximizing UAV data transmission efficiency subject to constraints like UAV-satellite interference, UAV energy limits, and total UAV hover time, the method finds UAV power and hover schedules that enable coexistence with satellites sharing the same spectrum.

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Dynamic channel allocation method for unmanned aerial vehicles (UAVs) to efficiently use and manage limited spectrum for controlling UAVs in national airspace. It allows dynamically allocating and changing communication channels for UAVs as they fly between areas with different channel requirements. The method involves UAV ground control stations (GCS) requesting channels from the frequency authority before takeoff. During flight, if the UAV enters a new area requiring different channels, it requests a new set before entering. This avoids mid-flight channel changes. The authority checks channel availability. It balances static allocation vs dynamic requesting based on UAV density and spectrum needs.

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Method for efficiently operating multiple unmanned aerial vehicles (UAVs) in a limited frequency band dedicated to controlling UAVs in national airspace. The method involves dynamically allocating and managing the limited frequency resources for UAV control, allowing recovery and reuse of frequencies after operation. This supports efficient operation of multiple UAVs in a limited band versus fixed allocation. The method involves initial channel setup between ground station and UAV using manual or automatic methods.

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Efficient and low-cost interference mitigation for unmanned aerial vehicle (UAV) communications by allocating frequencies using semi-supervised learning. The method involves the ground control center scanning pre-used frequencies for interference, predicting idle ones, and switching UAV frequency to avoid interference. The center monitors UAV comms, predicts interference, and switches proactively. This improves UAV comms reliability by avoiding interference compared to fixed bands.

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Spectrum allocation method for unmanned aerial vehicle (UAV) ad hoc networks that maximizes spectrum efficiency and fairness. The method involves establishing an optimal spectrum response strategy through a game bidding mechanism. UAV clusters bid for spectrum resources and the winning clusters form a cooperative sensing network. This allows sharing of spectrum sensing data for better detection and reduces the false alarm rate. The bidding game ensures fair allocation by making each cluster's response strategy a best response to the others'. The optimal configuration involves balancing factors like cooperative sensing time, detection probability, and cluster count.

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Efficiently using limited frequency resources for unmanned aerial vehicles (UAVs) by dynamically allocating additional frequencies for high-bandwidth missions. The method involves initially using a minimum frequency for takeoff/landing control. Then, when the UAV begins a mission requiring large data transfers, it allocates extra frequencies from ground control equipment dedicated to missions. This allows efficient frequency reuse and prevents saturation as the number of UAVs increases. The ground control equipment can swap roles between takeoff/landing and mission control to adaptively allocate resources based on UAV needs.

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Method for optimal allocation of unmanned aerial vehicle (UAV) channels based on spectrum cognition to improve reliability and continuity of UAV communications. The method involves using the UAV itself as a spectrum sensing node to scan and monitor the frequency bands used by the UAV. The sensing data is transmitted back to the ground where it's analyzed to predict idle channels. The predicted channels are then sent to the UAV to automatically switch to. This ensures the UAV continues communication on uninterfered channels. The UAV has separate radios for control signals and video/image transmission on different bands to avoid interference.

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Interference coordination scheme for wireless communications to/from aerial vehicles in LTE/5G networks. It uses coordinated allocation of dedicated time-frequency resources for aerial vehicles by neighboring base stations to reduce interference. A serving base station dedicates a subset of resources for aerial vehicles and signals neighboring base stations to do the same. Neighbors reduce activities in those resources. This prevents interference to aerial vehicle connections by concentrating their communications in specific resources.

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Method for detecting and selecting an operating frequency in a licensed band for unmanned aerial vehicle (UAV) missions. The method involves having the UAV's communication module sense for available channels in the licensed band during operation. If it doesn't find a channel, it waits and tries again. This allows the UAV to dynamically find an unused channel in the licensed band without needing manual frequency assignment. It also avoids disassembling the UAV each time to change frequencies. The ground control equipment broadcasts a beacon to identify the UAV's module and provide the licensed band channels for sensing.

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Method for optimizing spectrum management and reuse in scenarios involving communication between multiple users of moving platforms and ground stations using directional antennas. The method involves dynamically allocating frequency bands in real-time to maximize spectrum reuse while minimizing interference between moving platforms and ground stations. It takes into account factors like frequency spectrum availability, preferred utilization order, platform locations, velocities, antenna gains, and user priorities. The method involves generating a dynamic user-to-user interference matrix that updates in real-time. The output is frequency and bandwidth allocations for each link that yield maximal reuse factors with minimal computational demand.

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Efficiently using and managing spectrum for unmanned aerial vehicle (UAV) control in national airspace. The method involves dynamically allocating, returning, and reallocating UAV control and video channels instead of fixedly assigning them. It allows multiple UAVs to share limited UAV control bandwidth. The method involves centralized channel allocation by a frequency authority based on interference analysis. UAVs request channels, and if denied, fallback plans are provided. Channels are returned after landing. This allows efficient channel reuse.

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Adaptive frequency selection for drones to improve communication reliability by dynamically choosing the best frequency based on factors like location, altitude, and time. It involves calculating probability matrices for frequency points at different locations, altitudes, and times based on historical data. The matrices are combined and the highest probability frequency is selected as the drone's current frequency.

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UAV communication frequency band allocation system and method based on a semi-supervised learning algorithm to improve UAV communication reliability by dynamically switching frequency bands to avoid interference. The system uses a ground control center and UAV with shared wireless modules and spectrum sensors. The ground center scans for interference on pre-used bands. UAVs detect their own bands and predict idle ones to switch if interference occurs. This allows quick frequency hopping to avoid interference without disrupting normal operation. The method involves half-supervised learning to handle unlabeled spectrum data.

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Efficiently managing the available RF spectrum in an area for cooperative and non-cooperative wireless devices to reduce interference and improve spectrum utilization. The technique involves a spectrum manager that detects devices, receives spectrum requests, and intelligently allocates spectrum based on usage requirements. It coordinates devices using spectrum allocation rules to avoid conflicts and efficiently utilize both licensed and unlicensed spectrum.

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Allocation of communication ports for multi-drone systems to improve efficiency and reliability of drone task execution while preventing conflicts in the shared frequency bands. The method involves dividing frequency bands into groups based on equal distribution, assigning ports to each group, balancing load between ports, verifying drone identity, and dynamically allocating free bands to drones. This balances load, reduces conflicts, and prevents blockage by optimizing port usage and coordinating frequency assignments.

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Dynamic allocation of radio frequency spectrum for wireless communications using reinforcement learning and neural networks to optimize spectrum utilization. It involves constructing a radio communication system model, determining allocatable bands, key performance indicators, and priorities for scenarios. Reinforcement learning with deep neural networks allocate bands based on indicators and priorities to improve spectrum efficiency in scenarios like V2X, mobile user data, and D2D relay. It can also dynamically reallocate bands if communication quality falls below a threshold.

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Efficient and reliable multi-drone scheduling with improved communication by assigning tasks to drones based on their capabilities and allocating communication bands dynamically. The drone's characteristics and task requirements are analyzed to determine the best match. The dispatch server divides the communication spectrum into frequency bands and assigns the appropriate band to the drone after verification. This avoids conflicts and blocks.

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System for coordinating shared access to spectrum based on interference to at least some users of the shared spectrum. The system involves an Access Frequency Coordinator (AFC) that determines spectrum availability for devices based on their location and characteristics, as well as known interference sources. The AFC retrieves parameters for high-priority users like incumbents, computes interference contours, and stores them. When a device requests spectrum, the AFC extracts contours exceeding a threshold, determines available frequencies, and transmits the response. This allows protecting incumbents from interference while enabling efficient spectrum sharing.

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Method for optimal allocation of UAV communication channels using spectrum sensing and machine learning to improve reliability and avoid interference. The method involves the UAV itself scanning the spectrum it uses for interference, sending that data back to the ground, and then predicting idle channels using machine learning. The ground then assigns the best channel to the UAV. This allows dynamic channel switching to avoid interference and maintain continuous communication. The UAV has separate radios for control and data transmission on different bands to further reduce interference.

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Efficient spectrum sharing for cognitive drones that improves spectrum utilization and alleviates spectrum shortage. The method involves joint optimization of perception performance and transmission power for drones in different locations. It considers the impact of drone position on transmission mode and provides algorithms to find the optimal power and perception parameters for drones. The optimization balances interference mitigation and detection probability.

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A method for coordinating shared access to spectrum based on interference to at least some users of the shared spectrum. The method involves an automated frequency coordinator (AFC) retrieving parameters for high-priority users in the spectrum, computing interference contour values based on those parameters, storing the contours, and using them to determine available frequencies for lower-priority users based on their location uncertainties. This allows protecting incumbent users from interference while allowing dynamic sharing for others.

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