Publications

This work introduces a data-driven framework for rate control and applies it to high frequency (HF) communication systems that propagate via the Earth’s ionosphere. The rate control approach uses statistical techniques to forecast channel state with an autoregressive (AR) model, which has previously been applied to different forms of wireless fading, including "medium" timescale fading at HF. The objective of rate control is to maximize the data rate while constraining the rate of packets decoded in error. We show that under ideal assumptions, the rate controller selects the rate by backing off from the forecast average signal-to-noise ratio (SNR) by a factor of sigmaQ^-1(Beta), where sigma^2 correlates with fading variance, Beta denotes a constraint on decoder errors, and Q(.) is the complementary cumulative distribution function of the Gaussian distribution. Simulation results on an HF channel model show that compared with naive schemes, AR forecasting provides a good balance between achieving high rate and ensuring reliability.

In this work, we detail a synchronized channel hopping network for autonomous swarms of small unmanned aerial vehicles (UAVs) conducting intelligence, surveillance, and reconnaissance (ISR) missions in the presence of interference and jamming. The core component of our design is Queue Length Informed Maximal Matching (QLIMM), a distributed transmission scheduling protocol that exchanges queue state information between nodes to assign subdivisions of the swarm to orthogonal hopping patterns in response to the network’s throughput demands. QLIMM efficiently allocates channel resources across large networks without relying on any centralized control or pre-planned traffic patterns, which is in the spirit of a swarming capability. However, given that the control messaging must scale up with the swarm’s size and the challenging interference environments we consider, fragility could be a concern. To observe under what conditions control fails, we test our protocol against both simulated partial-band noise jamming and background interference. For the latter, we use data collected from a small unmanned aircraft system to characterize the interference seen by a UAV in the 2.4 and 5 GHz bands in both urban and rural settings. These measurements show that the interference can be 15 dB higher at a 50-meter flight altitude when compared to observations on the ground. Using this data, we conduct extensive network simulations of QLIMM in Riverbed Modeler to show that, under moderate jamming and interference, it outperforms traditional channel access methods as well as other scheduling protocols that do not pass queue state information.