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1. Leveraging UAV Rotation To Increase Phase Coherency in Distributed Transmit Beamforming

   https://doi.org/10.1109/CCNC49033.2022.9700554  

   Badi, Mahmoud ; Matson, N. Cameron ; Rajan, Dinesh ; Camp, Joseph ( January 2022 , 2022 IEEE 19th Annual Consumer Communications & Networking Conference (CCNC))

   Distributed transmit beamforming (DTBF) can allow a swarm of unmanned aerial vehicles (UAVs) to send a common message to a distant target. DTBF among N nodes can provide N 2 times the received power compared to a single node and can reduce interference by confining the signal in a certain direction. However, DTBF requires time, frequency, and phase synchronization. Here, we focus on the issue of phase incoherence at the distributed transmit nodes from two sources—different local oscillators (LOs) and hovering position movement—and how to counteract their impact at the receiver via local decisions, namely, rotation. To investigate how the UAV body and its rotation can affect phase coherency, we conduct controlled in-field experiments where we control the phase offset at two distributed antennas and measure the received signal level at four antenna positions on a drone for various rotation angles. We show that significant improvements can be achieved at the receiver through rotation. We also show that there exists an optimal combination of UAV rotation angle and antenna position on the drone to mitigate the effects of phase incoherence among the distributed transmitters. Finally, we demonstrate an interesting trade-off where, due to the heterogeneous nature of the UAV body, rotation angles that yield maximum beamforming gains might not result in the best average (or minimum) beamformed signal level across all possible phase errors at the distributed transmitters. 

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2. Distributed Approaches for Inter-Cell Interference Coordination in UAV-Based LTE-Advanced HetNets

   https://doi.org/10.1109/VTCFall.2018.8691002  

   Singh, Simran ; Kumbhar, Abhaykumar ; Guvenc, Ismail ; Sichitiu, Mihail L ( August 2018 , IEEE Vehicular Technology Conference)

   Unmanned aerial vehicles (UAVs) can supplement the existing ground-based heterogeneous cellular networks (Het-Nets), by replacing/supporting damaged infrastructure, providing real-time video support at the site of an emergency, offloading traffic in congested areas, extending coverage, and filling coverage gaps. In this paper, we introduce distributed algorithms that leverage UAV mobility, enhanced inter-cell interference coordination (ICIC), and cell range expansion (CRE) techniques defined in 3GPP Release-10 and 3GPP Release-11. Through Monte-Carlo simulations, we compare the system-wide 5th percentile spectral efficiency (5pSE) while optimizing the performance using a brute force algorithm, a heuristic-based sequential algorithm, and a deep Q-learning algorithm. The autonomous UAVs jointly optimize their location, ICIC parameters, and CRE to maximize 5pSE gains and minimize the outage probability. Our results show that the ICIC technique relying on a simple heuristic outperforms the ICIC technique based on deep Q-learning. Taking advantage of the multiple optimization parameters for interference coordination, the heuristic based ICIC technique can achieve 5pSE values that are reasonably close to those achieved with exhaustive brute force search techniques, at a significantly lower computational complexity. 

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3. Direction Estimation in 3D Outdoor Air–Air Wireless Channels through Machine Learning

   https://doi.org/10.3390/s23239524  

   Syed, Muhammad Hashir ; Singh, Maninderpal ; Camp, Joseph ( December 2023 , Sensors)

   UAVs need to communicate along three dimensions (3D) with other aerial vehicles, ranging from above to below, and often need to connect to ground stations. However, wireless transmission in 3D space significantly dissipates power, often hindering the range required for these types of links. Directional transmission is one way to efficiently use available wireless channels to achieve the desired range. While multiple-input multiple-output (MIMO) systems can digitally steer the beam through channel matrix manipulation without needing directional awareness, the power resources required for operating multiple radios on a UAV are often logistically challenging. An alternative approach to streamline resources is the use of phased arrays to achieve directionality in the analog domain, but this requires beam sweeping and results in search-time delay. The complexity and search time can increase with the dynamic mobility pattern of the UAVs in aerial networks. However, if the direction of the receiver is known at the transmitter, the search time can be significantly reduced. In this work, multi-antenna channels between two UAVs in A2A links are analyzed, and based on these findings, an efficient machine learning-based method for estimating the direction of a transmitting node using channel estimates of 4 antennas (2 × 2 MIMO) is proposed. The performance of the proposed method is validated and verified through in-field drone-to-drone measurements. Findings indicate that the proposed method can estimate the direction of the transmitter in the A2A link with 86% accuracy. Further, the proposed direction estimation method is deployable for UAV-based massive MIMO systems to select the directional beam without the need to sweep or search for optimal communication performance. 

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4. SwarmShare: Mobility-Resilient Spectrum Sharing for Swarm UAV Networking in the 6 GHz Band

   https://doi.org/10.1109/SECON52354.2021.9491602  

   Hu, Jiangqi ; Moorthy, Sabarish Krishna ; Harindranath, Ankush ; Guan, Zhangyu ; Mastronarde, Nicholas ; Bentley, Elizabeth Serena ; Pudlewski, Scott ( July 2021 , 2021 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON))

   null (Ed.)

   To mitigate the long-term spectrum crunch problem, the FCC recently opened up the 6 GHz frequency band for unlicensed use. However, the existing spectrum sharing strategies cannot support the operation of access points in moving vehicles such as cars and UAVs. This is primarily because of the directionality-based spectrum sharing among the incumbent systems in this band and the high mobility of the moving vehicles, which together make it challenging to control the cross-system interference. In this paper we propose SwarmShare, a mobility-resilient spectrum sharing framework for swarm UAV networking in the 6 GHz band. We first present a mathematical formulation of the SwarmShare problem, where the objective is to maximize the spectral efficiency of the UAV network by jointly controlling the flight and transmission power of the UAVs and their association with the ground users, under the interference constraints of the incumbent system. We find that there are no closed-form mathematical models that can be used characterize the statistical behaviors of the aggregate interference from the UAVs to the incumbent system. Then we propose a data-driven three-phase spectrum sharing approach, including Initial Power Enforcement, Offline-dataset Guided Online Power Adaptation, and Reinforcement Learning-based UAV Optimization. We validate the effectiveness of SwarmShare through an extensive simulation campaign. Results indicate that, based on SwarmShare, the aggregate interference from the UAVs to the incumbent system can be effectively controlled below the target level without requiring the real-time cross-system channel state information. The mobility resilience of SwarmShare is also validated in coexisting networks with no precise UAV location information. 

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5. Experimental Evaluation of Antenna Polarization and Elevation Effects on Drone Communications

   https://doi.org/10.1145/3345768.3355916  

   Badi, Mahmoud ; Wensowitch, John ; Rajan, Dinesh ; Camp, Joseph ( November 2019 , Proceedings of the 22nd ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems (ACM MSWiM))

   In the next wave of swarm-based applications, unmanned aerial vehicles (UAVs) need to communicate with peer drones in any direction of a three-dimensional (3D) space. On a given drone and across drones, various antenna positions and orientations are possible. We know that, in free space, high levels of signal loss are expected if the transmitting and receiving antennas are cross polarized. However, increasing the reflective and scattering objects in the channel between a transmitter and receiver can cause the received polarization to become completely independent from the transmitted polarization, making the cross-polarization of antennas insignificant. Usually, these effects are studied in the context of cellular and terrestrial networks and have not been analyzed when those objects are the actual bodies of the communicating drones that can take different relative directions or move at various elevations. In this work, we show that the body of the drone can affect the received power across various antenna orientations and positions and act as a local scatterer that increases channel depolarization, reducing the cross-polarization discrimination (XPD). To investigate these effects, we perform experimentation that is staged in terms of complexity from a controlled environment of an anechoic chamber with and without drone bodies to in-field environments where drone-mounted antennas are in-flight with various orientations and relative positions with the following outcomes: (i.) drone relative direction can significantly impact the XPD values, (ii.) elevation angle is a critical factor in 3D link performance, (iii.) antenna spacing requirements are altered for co-located cross-polarized antennas, and (iv.) cross-polarized antenna setups more than double spectral efficiency. Our results can serve as a guide for accurately simulating and modeling UAV networks and drone swarms. 

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