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.

翻译：由于无线用户数量的持续增长，频谱共享技术在无线生态系统中的应用日益普遍，其主要目标是避免对共存通信系统造成有害干扰。在考虑诸如海上及空中编队雷达等系统时，该问题尤为重要——这些系统中既有无线电设备运行着关键任务通信链路。为研究、开发和验证这些解决方案，合适的平台（如Colosseum无线网络仿真器）至关重要，因为它们能够在可控环境中实现频谱共享异构无线电技术的实验。本文展示了如何利用Colosseum对商用无线电波形进行孪生，以评估此类技术在复杂无线传播环境中的共存性能。为此，我们在Colosseum上构建了一个高保真频谱共享场景，评估孪生商用雷达波形对运行在CBRS频段的蜂窝网络的影响。随后，利用测试平台采集的IQ样本训练了一个运行于基站的机器学习智能体，用于检测既有雷达发射信号的存在并腾出带宽，以避免对其造成有害干扰。结果表明，平均检测准确率为88%，在信噪比高于0 dB且信干噪比高于-20 dB的区域中准确率超过90%，平均检测时间为137毫秒。