Signal Steering and Beamforming for Drone Communications

Millimeter wave non-orthogonal multiple access beam management for multi-drone networks. The method involves optimizing beamforming for a master drone and multiple slave drones using channel information. It groups the slaves based on angle and solves analog and digital beamforming vectors to align beams. This enables accurate and quick beam management in high-dynamic multi-drone systems.

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Enhancing wireless communication for unmanned aerial vehicles (UAVs) using directional antennas and flight path information. The directional antennas mitigate interference by redirecting the main beam towards base stations while minimizing interference from other sources. Flight path information is used to optimize handover and interference mitigation for UAVs by leveraging the predictable aerial channel environment. This allows tailoring network parameters like handover triggers and reporting sets for different flight regions.

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Beamforming technique to improve wireless communication links between airspace network base stations and aerial vehicles like urban air mobility (UAM) aircraft. The base stations select optimal beam directions based on the known flight paths of UAM vehicles to focus the signal towards them. This mitigates interference effects specific to the airspace network environment and improves link performance compared to omnidirectional transmission.

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IRS-assisted high-speed multi-UAV communication system beam tracking to enable reliable and effective communication between ground base stations and multiple high-speed moving UAVs. The method involves using intelligent reflecting surfaces (IRS) to help align beams between the base station and UAVs. It involves predicting the UAV spatial angles, optimizing base station and IRS beamforming matrices to maximize edge UAV SNR, and aligning dual beams using the predicted angles. This improves UAV performance by leveraging IRS to compensate for mobility and external factors.

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Relay forwarding method and system for unmanned aerial vehicles (UAVs) with integrated sensing capabilities to improve millimeter wave (mmWave) relay performance. The method involves using the UAV's sensing data to accurately form mmWave beams at the user position. This mitigates errors from integrated sensing. First, the user broadcasts a centimeter wave signal, the base station feeds back confirmation, and the user selects the strongest one. Then, the UAV flies to a position with line-of-sight to both. The UAV adjusts its mmWave reflector angles based on sensed user location to form a beam. By combining centimeter feedback and sensing, the UAV finds the angles with max mmWave power at the user.

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Adaptive beam tracking method for unmanned aerial vehicle (UAV) communication that improves efficiency and performance by dynamically adjusting training frequency based on UAV motion state and optimizing beam width using a layered codebook. The steps are: 1) Estimate UAV motion state, 2) Adjust training frequency based on motion state (higher for fast motion), 3) Optimize beam width using a layered codebook, 4) Select optimal beam width based on motion state, 5) Update codebook and weights with feedback. This adapts to UAV motion and terrain for better tracking and reduces training vs fixed methods. However, it requires motion estimation and energy for adaptation.

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Sparse array of unmanned aerial vehicles (UAVs) for beamforming in three-dimensional space. The array has sparsely distributed UAVs with arbitrary spacing between them. Each UAV has an antenna. The UAVs communicate wirelessly. This allows flexible, adaptive beamforming by controlling the UAV positions and antenna phases. It enables efficient, cooperative beamforming in complex 3D environments with discontinuous spaces due to obstructions.

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Beamforming method for unmanned aerial vehicle (UAV) communications that allows rapid and accurate beam alignment between a command UAV and a mission UAV. The method uses geographic information system (GIS) data from the mission UAV to enable the command UAV to generate narrow beams pointing directly at the mission UAV. This improves tracking accuracy, UAV endurance, and data rate compared to existing beamforming techniques. The mission UAV periodically sends its GIS location to the command UAV, which uses that data to steer beams precisely at the mission UAV.

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Efficient beamforming technique for radar-communication combined systems in drones. The technique allows drones with radar and communication capabilities to operate effectively in close proximity. It involves optimizing the radar and communication beam patterns using a common design based on signal-to-noise ratio (SNR) and transmit power. This enables simultaneous radar and communication using the same beams from the drone's beamforming device.

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Multi-front switching beamforming method for active antenna arrays covering the entire airspace. It compensates for phase differences between antenna arrays when switching between fronts to maintain phase consistency for beamforming. This avoids phase jitter issues during multi-front array switching that can lead to phase inconsistency. The phase compensation algorithm is applied to the steering vectors of each front to adjust them based on the relative phase differences between the antenna arrays.

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Reducing transmission power of unmanned aerial vehicles (UAVs) in non-orthogonal multiple access (NOMA) networks using intelligent reflectors to improve UAV endurance. The method involves jointly optimizing UAV beamforming and reflector phase shifts to minimize UAV transmit power. It converts the non-convex optimization problem into a semi-definite programming form and uses alternating optimization with randomization to approximate the solutions.

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Unmanned aerial vehicle (UAV) measurement and control method using intelligent antennas to improve UAV communication quality, range, and reliability. The method involves using an intelligent antenna array on the UAV that can adaptively steer beams in real-time based on signal analysis. This allows the UAV to dynamically align the antenna beams with ground stations and base stations to improve link quality, reduce interference, and mitigate multipath issues. The method aims to provide stable, high-quality UAV communication links using intelligent antennas.

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Beamforming method for decoupling logic beams and physical antennas in unmanned combat scenarios that allows stable communication links between drones. The method involves using a barrel-shaped array antenna that can steer beams by selecting specific antennas facing a target direction. This allows creating equal-gain seamless coverage in any horizontal plane without needing to rotate the entire array or splicing multiple arrays. This prevents issues like communication interruptions due to beam misalignment from drone movement or wind.

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Method for establishing direct communication using an unmanned aerial vehicle (UAV) with a reconfiguration intelligent surface (RIS) to provide reliable connectivity in emergency scenarios where UAVs may be deployed to areas with limited infrastructure. The method involves configuring RIS parameters based on compensating for UAV position and orientation oscillations to steer signal reflections to target areas. This mitigates loss due to uncertainty in UAV location. The RIS parameters are optimized using algorithms that consider second-order statistics of UAV vibrations.

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Applying millimeter wave (mmWave) wireless networking technology to unmanned aerial vehicles (UAVs) to enable high-speed, high-capacity UAV communications using mmWave beams. The method involves having UAVs generate beam tables based on received mmWave beams, monitor conditions for switching beams, determine target positions, move between beams, and update beam tables. The base stations transmit beams and adaptively adjust beam shapes. This allows UAVs to efficiently traverse mmWave networks with beam switching and handover.

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Adaptive beam tracking method for unmanned aerial vehicle (UAV) communications in high-speed aerial scenarios like UAV combat. The method involves generating tracking beams using channel state information (CSI) from the UAVs, but then correcting the beam directions using known flight paths of the UAVs. This improves accuracy compared to just using CSI alone. The flight path information allows adjusting the beam angles in real-time to compensate for UAV motion. It's particularly useful in dynamic aerial combat scenarios where the UAVs are moving quickly and unpredictably.

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Efficient mmWave 5G communications for unmanned aerial vehicles (UAVs) that can provide high data rate uplinks to the base station while avoiding interference to satellite receivers. The method involves determining optimal uplink beams between UAV antennas and base station receivers, since mmWave base stations with upward-facing antennas can provide higher gain to serve UAVs above horizon. The UAV provides spatial and antenna info to the base station, which configures a beam sweep of candidate pairs. The UAV then performs the sweep to find the best pair for uplink. This allows UAVs to connect to base stations with upward-facing receivers for high gain uplinks, without causing interference to satellite receivers above horizon.

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Method for enhancing communication range and quality of UAVs by using an azimuth tracking device and array antenna. The UAV initializes the azimuth of the tracking device and beam direction of the array antenna. It then uses the UAV's position to control the tracking device to compensate for horizontal angle offsets. This compensates for signal attenuation due to propagation distance by keeping the UAV's azimuth aligned with the receiver's location.

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Robust millimeter wave beamforming for unmanned aerial vehicles (UAVs) that can provide reliable and stable communication links despite UAV jitter. The method involves designing hybrid analog-digital beamforming networks for UAV base stations that can adapt to UAV motion. It models UAV jitter to predict how the UAV's motion affects the angle of signal emission. A wide beam is designed to cover the entire range of possible emission angles due to jitter. Digital beamforming is then optimized using techniques like channel estimation and power injection to balance beam width and gain. This ensures fairness and accessibility for users at all distances.

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Multi-beamforming system for tracking drone positions using fewer serial links compared to conventional systems. The system consists of a chain of beam steering blocks that receive RF signals from the drone. Each block computes partial sum beams from 1 to M indices. The blocks combine the partial sums from the previous block with their own to update the accumulated beams. The final block outputs the accumulated beams for drone location tracking. This allows tracking with fewer serial links compared to parallel transmission from all blocks.

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