The ever-growing number of wireless communication devices and technologies demands spectrum-sharing techniques. Effective coexistence management is crucial to avoid harmful interference, especially with critical systems like nautical and aerial radars in which incumbent radios operate mission-critical communication links. In this demo, we showcase a framework that leverages Colosseum, the world's largest wireless network emulator with hardware-in-the-loop, as a playground to study commercial radar waveforms coexisting with a cellular network in CBRS band in complex environments. We create an ad-hoc high-fidelity spectrum-sharing scenario for this purpose. We deploy a cellular network to collect IQ samples with the aim of training an ML agent that runs at the base station. The agent has the goal of detecting incumbent radar transmissions and vacating the cellular bandwidth to avoid interfering with the radar operations. Our experiment results show an average detection accuracy of 88%, with an average detection time of 137 ms.

翻译：摘要：无线通信设备与技术的持续增长对频谱共享技术提出了需求。有效的共存管理对于避免有害干扰至关重要，特别是在航海雷达和航空雷达等关键系统中，其现有无线电设备运行着任务关键型通信链路。本演示中，我们展示了一个框架，利用全球最大的硬件在环无线网络仿真器Colosseum作为试验平台，研究复杂环境下商用雷达波形与蜂窝网络在CBRS频段的共存。为此，我们构建了一个自组织的高保真频谱共享场景。通过部署蜂窝网络采集IQ样本，旨在训练一个运行于基站的机器学习智能体。该智能体的目标是检测现有雷达信号传输，并释放蜂窝带宽以避免对雷达运行造成干扰。实验结果表明，平均检测准确率达到88%，平均检测时间为137毫秒。