Sensor-Assisted Rate Adaptation for UAV MU-MIMO Networks

TL;DR

This paper introduces SensRate, a sensor-assisted rate adaptation algorithm for UAV MU-MIMO networks that leverages onboard sensors for improved channel prediction and interference estimation, significantly enhancing throughput in dynamic air-to-ground links.

Contribution

The paper presents a novel UAV-specific MU-MIMO rate adaptation algorithm that uses onboard sensors for channel prediction without additional hardware costs.

Findings

Achieves 1.24x and 1.28x throughput gains over existing algorithms.

Effectively predicts channels using flight sensors in highly dynamic environments.

Demonstrated on a commercial UAV with significant performance improvements.

Abstract

Propelled by multi-user MIMO (MU-MIMO) technology, unmanned aerial vehicles (UAVs) as mobile hotspots have recently emerged as an attractive wireless communication paradigm. Rate adaptation (RA) becomes indispensable to enhance UAV communication robustness against UAV mobility-induced channel variances. However, existing MU-MIMO RA algorithms are mainly designed for ground communications with relatively stable channel coherence time, which incurs channel measurement staleness and sub-optimal rate selections when coping with highly dynamic air-to-ground links. In this paper, we propose SensRate, a new uplink MU-MIMO RA algorithm dedicated for low-altitude UAVs, which exploits inherent onboard sensors used for flight control with no extra cost. We propose a novel channel prediction algorithm that utilizes sensor-estimated flight states to assist channel direction prediction for each client and estimate inter-user interference for optimal rates. We provide an implementation of our design using a commercial UAV and show that it achieves an average throughput gain of 1.24\times and 1.28\times compared with the bestknown RA algorithm for 2- and 3-antenna APs, respectively