Twinning Commercial Radio Waveforms in the Colosseum Wireless Network Emulator

TL;DR

This paper demonstrates how the Colosseum wireless network emulator can twin commercial radio waveforms to evaluate spectrum-sharing scenarios, using machine learning for radar detection to prevent interference in complex environments.

Contribution

It introduces a high-fidelity spectrum-sharing scenario on Colosseum and applies machine learning to detect incumbent radars, enabling safer coexistence of wireless technologies.

Findings

Detection accuracy of 88% for radar signals

Over 90% accuracy at SNR above 0 dB

Average detection time of 137 ms

Abstract

Because of the ever-growing amount of wireless consumers, spectrum-sharing techniques have been increasingly common in the wireless ecosystem, with the main goal of avoiding harmful interference to coexisting communication systems. This is even more important when considering systems, such as nautical and aerial fleet radars, in which incumbent radios operate mission-critical communication links. To study, develop, and validate these solutions, adequate platforms, such as the Colosseum wireless network emulator, are key as they enable experimentation with spectrum-sharing heterogeneous radio technologies in controlled environments. In this work, we demonstrate how Colosseum can be used to twin commercial radio waveforms to evaluate the coexistence of such technologies in complex wireless propagation environments. To this aim, we create a high-fidelity spectrum-sharing scenario on Colosseum to evaluate the impact of twinned commercial radar waveforms on a cellular network operating in the CBRS band. Then, we leverage IQ samples collected on the testbed to train a machine learning agent that runs at the base station to detect the presence of incumbent radar transmissions and vacate the bandwidth to avoid causing them harmful interference. Our results show an average detection accuracy of 88%, with accuracy above 90% in SNR regimes above 0 dB and SINR regimes above -20 dB, and with an average detection time of 137 ms.