Spectrum Management for Drone Operations

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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Frequency band scheduling strategy for a multi-IRS-UAV assisted wireless power supply communication system that maximizes sum rate and reduces interference. The strategy involves dividing the spectrum into sub-bands, defining interference ranges for receivers, and iteratively selecting optimal bands for each device using successive fixation. This improves system quality by minimizing co-channel interference and reducing single-frequency interference risk.

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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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Intelligent interference forecasting system (IIFS) to enable intelligent spectrum management for wireless devices. The IIFS aims to provide near real-time spectrum monitoring and prediction to optimize spectrum utilization and mitigate interference. It involves a centralized server that collects interference data from deployed devices, identifies non-interfering bandwidths, and dynamically allocates spectrum to new devices based on the stored interference information. This provides a holistic view of interference across sectors and facilitates efficient spectrum sharing.

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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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