In this work we propose SERENADE: a large-scale, real-time, and flexible emulator for satellite networking. While existing tools rely on heavier container-based node emulation, SERENADE is written in Go, and models nodes using ultra-lightweight threads. This design enables SERENADE to realistically emulate up to several 100,000s of nodes (both satellite and ground nodes) on a single machine, a first of its kind. We ensure that scale does not come at the expense of responsiveness; SERENADE starts up in a few seconds and tracks satellite trajectories in real time (rather than being pre-computed), making it ultra-responsive to changes on-the-fly. Additionally, SERENADE is designed to interface easily with external applications, allowing for arbitrary and dynamic network inputs and measurements. These features make it the first satellite network emulator suitable to operate as a digital twin for satellite networking. Our evaluations highlight SERENADE’s ability to efficiently scale while maintaining a high degree of networking realism and remaining adaptable. We demonstrate SERENADE’s digital twin capabilities through several real-world case studies, including disaster relief and satellite constellation updates. We also use SERENADE to illustrate how many proposed frameworks for user-satellite association fail to effectively scale to large deployment scenarios and result in grossly under-utilized network resources.

Mobile networks have increased spectral efficiency through advanced multiplexing strategies that are coordinated by base stations (BS) in licensed spectrum. However, external interference on clients leads to significant performance degradation during dynamic (unlicensed) spectrum access (DSA). We introduce the notion of network tomography for DSA, whereby clients are transformed into spectrum sensors, whose joint access statistics are measured and used to account for interfering sources. Albeit promising, performing such tomography naively incurs an impractical overhead that scales exponentially with the multiplexing order of the strategies deployed – which will only continue to grow with 5G/6G technologies.To this end, we propose a novel, scalable network tomography framework called NeTo-X that estimates joint client access statistics with just linear overhead, and forms a blue-print of the interference, thus enabling efficient DSA for future networks. NeTo-X’s design incorporates intelligent algorithms that leverage multi-channel diversity and the spatial locality of interference impact on clients to accurately estimate the desired interference statistics from just pair-wise measurements of its clients. The merits of its framework are showcased in the context of resource management and jammer localization applications, where its performance significantly outperforms baseline approaches and closely approximates optimal performance at a scalable overhead.

Radio environment maps provide a comprehensive spatial view of the wireless channel and are especially useful in on-demand UAV wireless networks where operators are not afforded the typical time spent planning base station deployments (e.g. emergency response). Equipped with an accurate radio environment map, a mobile UAV can quickly locate to an optimal location to serve users on the ground. Machine learning has recently been proposed as a tool to create radio environment maps from satellite images of the target environment. However, the highly dynamic nature that precipitates most on-demand aerial network deployments likely renders the satellite image data available for the environment to be inaccurate. In this paper we present, OREMAN, a hybrid offline-online system for aerial radio environment map creation which leverages a common sensing modality present on most UAVs: visual cameras. OREMAN combines a suite of off-line trained neural network models with an adaptive trajectory planning algorithm to iteratively predict/refine the radio environment map and estimate the most valuable trajectory locations. By using UAV vision, OREMAN arrives at a highly accurate map much faster and with fewer measurements than other approaches, and is very effective even in scenarios where no prior environmental knowledge is available.

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 N2times 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.

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.