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

合作研究：SWIFT：MEDUSA：用于在频谱共享期间检测现有企业的中频环境传感能力

• 批准号: 2229445
• 负责人: Jessica Ruyle
• 金额: $ 27.98万
• 依托单位: University of Oklahoma Norman Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-11-01 至 2025-10-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2229445&HistoricalAwards=false
• 关键词: Collaborative; Research; SWIFT; MEDUSA; Mid

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.

中频段频谱共享为商业5G运营商和未经许可的使用提供了前所未有的机会，可以利用理想的频率，尽管需要可靠地检测更高优先级的现有运营商。例如，在3.55-3.7GHz公民宽带无线电服务（CBRS）频带中通过环境感知能力（ESC）传感器检测海军舰载雷达信号的要求严重限制了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干扰物的中频环境传感能力，旨在检测静态/移动的雷达的存在以及并发和相对较高功率的5G和4G-LTE信号中的异常传输。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）来实现这一目标。MEDUSA将使ESC传感器能够与不同类型的无线信号一起工作，既可以用于单独的频谱监测，也可以通过协作方法提高现有检测的准确性。该项目将导致天线设计文件，数据集和学习算法的开源发布。该项目还包括一些推广和传播活动，例如在项目网站上发布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。该项目有三个目标，即在公民宽带无线电服务（CBRS）波段进行雷达检测，但也可推广到其他频率。首先，它提出了一个深度学习框架，以增强环境感知能力（ESC）传感器的辨别能力，同时通过使用频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，其功率比FCC规定的水平强5 dB。此外，PI将为未知条件开发迁移学习方法。其次，它将推进协同推理的科学，当多个ESC传感器通过使用作为第一个目标的一部分开发的算法融合个体预测来做出（i）独立和（ii）联合决策时。它还提出了一种融合频谱图和原始同相和正交（IQ）样本的方法。第三，当地理上分离且任意间隔的ESC传感器时间和相位同步时，它们形成用于接收波束形成的大规模虚拟阵列。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基站产生零陷。最后，研究目标将通过NSF高级无线研究平台（PAWR）在仿真和实验测试台上进行验证。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

项目成果

Implementation of Software-Defined Antenna and Radio Test System for Congested Spectral Environments

针对拥塞频谱环境实施软件定义天线和无线电测试系统

• 发表时间: 2023
• 期刊: GNU Radio Conference
• 作者: Agasti, Rosalind;Kim, Elliot;Ruyle, Jessica;Christopher Onwuchekwa, Chukwunodebem;Azizi, Shabnam;Rahaim, Michael
• 通讯作者: Rahaim, Michael