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  1. 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,more »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.« less
  2. Unmanned Aerial Vehicles (UAVs) often lack the size, weight, and power to support large antenna arrays or a large number of radio chains. Despite such limitations, emerging applications that require the use of swarms, where UAVs form a pattern and coordinate towards a common goal, must have the capability to transmit in any direction in three-dimensional (3D) space from moment to moment. In this work, we design a measurement study to evaluate the role of antenna polarization diversity on UAV systems communicating in arbitrary 3D space. To do so, we construct flight patterns where one transmitting UAV is hovering at a high altitude (80 m) and a receiving UAV hovers at 114 different positions that span 3D space at a radial distance of approximately 20 m along equally-spaced elevation and azimuth angles. To understand the role of diverse antenna polarizations, both UAVs have a horizontally-mounted antenna and a vertically-mounted antenna-each attached to a dedicated radio chain-creating four wireless channels. With this measurement campaign, we seek to understand how to optimally select an antenna orientation and quantify the gains in such selections.
  3. Existing rate adaptation protocols have advocated training to establish the relationship between channel conditions and the optimum modulation and coding scheme. However, innate with in-field operation is encountering scenarios that the rate adaptation mechanism has not yet encountered. Frequently, protocols are optimally tuned for indoor environments but, when taken outdoors, perform poorly. Namely, the decision structure formed by offline training, lacks the ability to adapt to a new situation on the fly. The changing wireless environment calls for a rate adaption scheme that can quickly infer the channel type and adjust accordingly. Typical SNR-based rate adaptation scheme do not capture the nuance of the performance variable in different channel types. In this paper, we propose a novel scheme that allow SNR-based rate selection algorithms to be trained online in the environment in which they are operating. Inspired by the idea that, to do well, an athlete must train for the type of athletic event and environment in which they are competing, we propose FIT, an on-the-fly, in-situ training mechanism for SNRbased protocols. To do so, we first propose the FIT framework which addresses the challenges of making rate decisions with unpredictable fluctuation and lack of repeatability of real wireless channels.more »To distinguish between channel types in the training, we then characterize wireless channels according to the link-layer performance and introduce a novel, computationally-efficient, channel performance manifold matching technique to infer the channel type given a sequence of throughput measurements for various link-level parameters. To evaluate our methods, we implement rate selection which uses FIT for training alongside channel performance manifold matching. We then perform extensive experiments on emulated and in-field wireless channels to evaluate the online learning process, showing that the rate decision structure can be updated as channel conditions change using existing traffic flows. The experiments are performed over multiple frequency bands. The proposed FIT framework can achieve large throughput gains compared to traditional SNRbased protocols (8X) and offline-training-based methods (1.3X), particularly in a dynamic wireless propagation environments that lack appropriate training.« less
  4. 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 tomore »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.« less