  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.
