Collaborative Research: SWIFT: MEDUSA: Mid-band Environmental Sensing Capability for Detecting Incumbents during Spectrum Sharing

中频频谱共享为商业5G运营商和未经许可的使用提供了前所未有的机会，尽管需要可靠地检测到更高优先级的现任者。例如，3.55-3.7 GHz民用宽带无线电服务(CBRS)频段的环境感知能力(ESC)传感器对舰载雷达信号的检测要求严重限制了5G运营商的传输功率。该项目名为MEDUSA：用于在频谱共享期间检测现任者的中频环境传感能力，其目标是检测并发和相对更高功率的5G和4G-LTE信号中是否存在静态/移动雷达和异常传输。它通过机器学习(对现有干扰做出反应)和接收器天线设计(主动避免干扰)来实现这一点。Medusa将使ESC传感器能够处理不同类型的无线信号，既可以用于个人频谱监测，也可以通过协作方法提高现有检测精度。该项目将导致为研究界公开发布天线设计文件、数据集和学习算法。它还包括几项外联和传播活动，例如在项目网站上托管与频谱专家的访谈录音，从代表性不足的群体中招募学生，以及设计使用CBRS相关数据集的课程项目。该项目为公民宽带无线电服务(CBRS)频段的雷达探测制定了三个目标，但也可推广到其他频率。首先，提出了一种深度学习框架来增强环境感知能力(ESC)传感器的区分能力，同时通过使用谱图输入来保护隐私。这些传感器将检测5G和4G-LTE信号中的雷达脉冲，功率比FCC规定的水平高5分贝。此外，PIS将开发针对未知条件的迁移学习方法。其次，它将促进协作推理的科学，当多个ESC传感器做出(I)独立和(Ii)联合决策时，通过使用作为第一个目标的一部分开发的算法来融合单独的预测。它还提出了一种将光谱图和原始同相和正交(IQ)样品融合的方法。第三，当地理上分离的任意间距的ESC传感器在时间和相位上同步时，它们形成了用于接收波束形成的海量虚拟阵列。PI将设计实时权重自适应算法和喇叭天线，可以对已知的5G/4G-LTE基站产生零点。最后，研究目标将通过NSF高级无线研究平台(PAWRT)在仿真和实验试验台上进行验证。他的获奖反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Spectrum sharing in mid-band offers unprecedented opportunity to harness desirable frequencies for commercial 5G operators and for unlicensed use, although higher priority incumbents need to be reliably detected. As an example, the requirement of detecting naval ship-borne radar signals by the environmental sensing capability (ESC) sensors in the 3.55-3.7GHz Citizens Broadband Radio Service (CBRS) band severely limits the transmission power for 5G operators. The objective of this project called MEDUSA: Mid-band Environmental sensing capability for Detecting incUmbents during Spectrum shAring is to detect the presence of static/mobile radar and anomalous transmissions within concurrent and comparatively higher power 5G and 4G-LTE signals. It achieves this through machine learning (reacting to existing interference) and receiver antenna design (proactively avoiding interference). MEDUSA will enable ESC sensors to work with different types of wireless signals, both for individual spectrum monitoring and via collaborative methods for enhanced incumbent detection accuracy. The project will result in open-source release of antenna design files, datasets and learning algorithms for the research community. It also includes several outreach and dissemination activities such as hosting recordings of interviews with spectrum experts on the project website, recruiting students from under-representative groups, and designing course projects that use CBRS-related datasets.The project has three goals for radar detection in the Citizens Broadband Radio Service (CBRS) band but is also generalizable for other frequencies. First, it proposes a deep learning framework to enhance the discriminative abilities of the environmental sensing capability (ESC) sensor while preserving privacy by using spectrogram inputs. These sensors will detect radar pulses within 5G and 4G-LTE signals with powers stronger than FCC-mandated levels by 5 dB. Furthermore, the PIs will develop transfer-learning methods for unseen conditions. Second, it will advance the science of collaborative inference, when multiple ESC sensors make (i) independent and (ii) joint decisions by fusing individual predictions using the algorithms developed as part of the first goal. It also proposes a method of fusion of spectrograms and raw in-phase and quadrature (IQ) samples. Third, when the geographically separated and arbitrarily spaced ESC sensors are time and phase synchronized, they form a massive virtual array for receive beamforming. The PIs will design real-time weight adaptation algorithms and horn antennas that can create nulls towards known 5G/4G-LTE base stations. Finally, the research goals will be validated in emulation as well as over experimental testbeds through the NSF Platform for Advanced Wireless Research (PAWRThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.