EAGER: ML-enabled early warning of blockage and beam transitions in mobile, hybrid sub-6GHz/mmWave systems

EAGER：支持 ML 的移动混合 6GHz/毫米波系统中的阻塞和波束转换预警

• 批准号: 2122012
• 负责人: Alexandra Duel-Hallen
• 金额: $ 9.99万
• 依托单位: North Carolina State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-15 至 2023-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2122012&HistoricalAwards=false
• 关键词: EAGER; ML; enabled; early; warning

The emerging generation of cellular communication, 5G, is expected to utilize frequencies in the range of tens to hundreds of GHz (mmWave) to overcome the bandwidth limitations inherent to 4G (sub-6 GHz) systems. However, mmWave signals do not propagate as far, are more susceptible to blocking by physical objects, and require more directional communication than 4G signals. Indeed, early deployments of commercial 5G mmWave networks in 2019 and 2020 have suffered from major coverage and penetration problems. This project evaluates the feasibility of a novel, potentially transformative approach to obtain an early warning of blockages and antenna beam transitions at mmWave (5G) using sub-6GHz (4G-like) observations. Suitability of machine learning (ML) for enabling this task is investigated. A realistic physics-based propagation model is enhanced to validate the proposed approach. The project is strengthened by the interdisciplinary PI team with combined expertise in communication theory, signal processing, and propagation physics. The proposed methods introduce innovations to advance the broader fields of 5G networks and mmWave propagation modeling. The insights of the proposed research will be integrated into courses and presentations to student organizations, and the outcomes published for professionals. A graduate student and undergraduate students will be trained in a diverse, multidisciplinary, and inclusive environment about vibrant wireless communications topics. Close collaboration with NSF BWAC and PAWR platforms will enhance the success of proposed research and outreach plans by dissemination of the research outcomes in the centers' events.This high-risk, high reward project develops novel digital signal processing (DSP) and ML solutions for solving resiliency problems in real-world mmWave deployments. These methods are suitable for hybrid communication systems, where the sub-6 GHz and mmWave bands are employed simultaneously. Using the Fresnel theory of diffraction and our accurate physics-based model, we have previously demonstrated that diffracted sub-6 GHz signals reach a specified received signal strength (RSS) threshold much earlier than mmWave signals. The latter property is exploited in this project to develop an early-warning method that forecasts blockage, beam direction, and other rapid changes in mmWave signals several to tens of milliseconds (several to hundreds of slots) ahead in mobile communications systems. The early-warning approach provides hybrid mobile communications systems with sufficient time to adapt the data rate, change the antenna direction, or perform a handover between the two frequencies or base stations before a significant change of the mmWave signal occurs. The early warning method relies solely on the physical properties of diffraction, not on measured environments or dense beams or users. It improves resilience of mobile mmWave networks to blockages and other rapid signal changes by continuously adapting to the environmental features, e.g., moving obstacles or small reflectors not captured by base-station siting, environmental mapping, and other previously proposed approaches. The early warning algorithm is trained and validated using an accurate spatiotemporal sub-6 GHz/mmWave hybrid channel model, which can provide a large set of physically realistic scenarios. Utilization of the physical model in this project will provide insights for collecting 'smart data' in future hybrid channel measurements using PAWR platform at NC State and online data repositories.This 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.

新兴一代蜂窝通信5G预计将利用数十至数百GHz（毫米波）范围内的频率来克服4G（低于6 GHz）系统固有的带宽限制。然而，毫米波信号传播得不那么远，更容易受到物理物体的阻挡，并且需要比4G信号更定向的通信。事实上，2019年和2020年商用5G毫米波网络的早期部署遭遇了重大的覆盖和渗透问题。该项目评估了一种新的、潜在的变革性方法的可行性，该方法使用低于6 GHz（类似4G）的观测结果在毫米波（5G）上获得阻塞和天线波束转换的早期预警。研究了机器学习（ML）实现这一任务的适用性。一个现实的基于物理的传播模型增强，以验证所提出的方法。该项目得到了跨学科PI团队的加强，该团队具有通信理论，信号处理和传播物理学方面的综合专业知识。所提出的方法引入了创新，以推进5G网络和毫米波传播建模的更广泛领域。拟议研究的见解将被整合到课程和演示给学生组织，并为专业人士发表的成果。研究生和本科生将在一个多元化，多学科和包容性的环境中接受关于充满活力的无线通信主题的培训。与NSF BWAC和PAWR平台的密切合作将通过在中心活动中传播研究成果来提高拟议研究和推广计划的成功率。这个高风险、高回报的项目开发了新型数字信号处理（DSP）和ML解决方案，用于解决现实世界毫米波部署中的弹性问题。这些方法适用于混合通信系统，其中同时采用子6 GHz和毫米波频带。使用菲涅耳衍射理论和我们精确的基于物理的模型，我们之前已经证明，衍射的6 GHz以下信号比毫米波信号更早达到指定的接收信号强度（RSS）阈值。在该项目中利用后一种特性来开发一种早期预警方法，该方法在移动的通信系统中提前几到几十毫秒（几到几百个时隙）预测阻塞、波束方向和毫米波信号的其他快速变化。预警方法为混合移动的通信系统提供了足够的时间，以在毫米波信号发生显著变化之前调整数据速率、改变天线方向或执行两个频率或基站之间的切换。预警方法仅依赖于衍射的物理特性，而不是测量环境或密集光束或用户。它通过不断适应环境特征，提高了移动的毫米波网络对阻塞和其他快速信号变化的恢复能力，例如移动的障碍物或小的反射体不能被基站选址、环境测绘和其他先前提出的方法捕获。使用精确的时空子6 GHz/mmWave混合信道模型来训练和验证预警算法，该模型可以提供大量的物理现实场景。该项目中的物理模型的利用将为在未来的混合通道测量中使用北卡罗来纳州的PAWR平台和在线数据存储库收集“智能数据”提供见解。该奖项反映了NSF的法定使命，并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，被认为值得支持。

项目成果

Early Warning of mmWave Signal Blockage Using Diffraction Properties and Machine Learning

利用衍射特性和机器学习对毫米波信号阻塞进行早期预警

• DOI: 10.1109/lcomm.2022.3204636
• 发表时间: 2022
• 期刊: IEEE Communications Letters
• 作者: Fallah Dizche, Amirhassan;Duel-Hallen, Alexandra;Hallen, Hans
• 通讯作者: Hallen, Hans