Enabling high data-rate uplink cellular connectivity for drones is a challenging problem, since a flying drone has a higher likelihood of having line-of-sight propagation to base stations that terrestrial UEs normally do not have line-of-sight to. This may result in uplink inter-cell interference and uplink performance degradation for the neighboring ground UEs when drones transmit at high data-rates (e.g., video streaming).

We address this problem from a cellular operator's standpoint to support drone-sourced video streaming of a point of interest. We propose a low-complexity, closed-loop control system for Open-RAN architectures that jointly optimizes the drone's location in space and its transmission directionality to support video streaming and minimize its uplink interference impact on the network.

We prototype and experimentally evaluate the proposed control system on an outdoor multi-cell RAN testbed. Furthermore, we perform a large-scale simulation assessment of the proposed system on the actual cell deployment topologies and cell load profiles of a major US cellular carrier. The proposed Open-RAN-based control achieves an average 19% network capacity gain over traditional BS-constrained control solutions and satisfies the application data-rate requirements of the drone (e.g., to stream an HD video).

UAV networks can provide a resilient communication infrastructure to enhance terrestrial networks in case of traffic spikes or disaster scenarios. However, to be able to do so, they need to be based on high-bandwidth wireless technologies for both radio access and backhaul. With this respect, the mmWave spectrum represents an enticing solution, since it provides large chunks of untapped spectrum that can enable ultra-high data-rates for aerial platforms.

Aerial mmWave channels, however, experience characteristics that are significantly different from terrestrial deployments in the same frequency bands. As of today, mmWave aerial channels have not been extensively studied and modeled. Specifically, the combination of UAV micro-mobility (because of imprecisions in the control loop, and external factors including wind) and the highly directional mmWave transmissions require ad hoc models to accurately capture the performance of UAV deployments.

To fill this gap, we propose an empirical propagation loss model for UAV-to-UAV communications at 60 GHz, based on an extensive aerial measurement campaign conducted with the Facebook Terragraph channel sounders. We compare it with 3GPP channel models and make the measurement dataset publicly available.

Networks of Unmanned Aerial Vehicles (UAVs), composed of hundreds, possibly thousands of highly mobile and wirelessly connected flying drones will play a vital role in future Internet of Things (IoT) and 5G networks. However, how to control UAV networks in an automated and scalable fashion in distributed, interference-prone, and potentially adversarial environments is still an open research problem.

SwarmControl introduces a new software-defined control framework for UAV wireless networks based on distributed optimization principles. In essence, SwarmControl provides the Network Operator (NO) with a unified centralized abstraction of the networking and flight control functionalities. High-level control directives are then automatically decomposed and converted into distributed network control actions that are executed through programmable software-radio protocol stacks.
SwarmControl (i) constructs a network control problem representation of the directives of the NO; (ii) decomposes it into a set of distributed sub-problems; and (iii) automatically generates numerical solution algorithms to be executed at individual UAVs.

We present a prototype of an SDR-based, fully reconfigurable UAV network platform that implements the proposed control framework, based on which we assess the effectiveness and flexibility of SwarmControl with extensive flight experiments. Results indicate that the SwarmControl framework enables swift reconfiguration of the network control functionalities, and it can achieve an average throughput gain of 159% compared to the state-of-the-art solutions.

High-speed cellular connectivity for drones (UAVs) is a key requirement for infrastructure monitoring and live broadcasting applications, among others. Different from ground mobile phones (UEs), however, UAVs benefit from unique line-of-sight conditions to multiple base stations (BSs), which may result in degraded performances for UEs in the surroundings. We experimentally evaluate this effect on a controlled LTE testbed, measuring up to 21.75 Mbps uplink throughput reduction for ground UEs in presence of UAVs.

To mitigate this effect, we propose a new approach designed to reduce interference to adjacent BSs through a combination of steerable directional transmitters and optimized flight control. We design a control mechanism to jointly optimize the trajectory of the drone and the directional orientation of the uplink transmission. Based on an empirical characterization of aerial signal propagation in 3D, the proposed control algorithms solve optimal trajectory problems on a directed graph representation of the aerial space. Our evaluation shows average interference reduction at neighboring BSs of 5.87 dB and average improvement of the drone signal-to-noise ratio of 9.23 dB compared to traditional channel-unaware flight control solutions employing omni-directional transmitters.

Mobile cells are seen as an enabler of more flexible and elastic services for next-generation wireless networks, making it possible to provide ad hoc coverage in failure scenarios and scale up the network capacity during peak traffic hours and temporary events.
When mounted on Unmanned Aerial Vehicles (UAVs), mobile cells require a high-capacity, low-latency wireless backhaul. Although mmWaves can meet such data-rate demand, they suffer from high-latency link establishment, due to the need to transmit and receive with highly directional beams to compensate for the high isotropic path loss.

mmBAC reviews the benefits of side-information-aided beam management and present a GPS-aided beam tracking algorithm for UAV-based aerial cells. mmBAC prototypes the proposed algorithm on a mmWave aerial link using a DJI M600 Pro and 60 GHz radios and prove its effectiveness in reducing the average link establishment latency by 66% with respect to state-of-the-art non-aided schemes.