ERI: Empowering Data-Driven Resource Management in Indoor 5G+ Wireless Networks

该奖项的全部或部分资金来自《2021年美国救援计划法案》(公法117-2)。未来的数据流量趋势要求高质量的无线连接速率达到每秒几千兆位，延迟小于10毫秒。然而，传统的无线电频谱相当拥挤，因此无法满足如此高的要求。因此，未使用的高频频段将在第五代及以后(5G)无线网络中采用。然而，高频段的无线连接受到用户移动性导致的频繁中断的挑战。最近的研究表明，基于人工智能的先进网管技术可以与用户移动性保持可靠的高质量链路。然而，为了开发这种先进的技术，需要全面的、高精度的无线信道质量数据集。不幸的是，研究界无法访问这些数据集。该项目的第一个目标是开发一个逼真、高精度的模拟器，生成400-800太赫兹频率范围内的5G无线信道的丰富数据集。该模拟器将公开提供，以支持数据驱动的5G网络管理解决方案的研究工作。生成的数据集将通过最先进的试验床进行验证。此外，生成的数据集将被表征以了解用户移动性模式对无线信道质量的影响。此外，还将开发新的方法来预测用户移动性导致的信道质量，这将进一步有助于开发有效的5G网络管理工具。通过支持数据驱动的网络管理解决方案的未来研究，该项目实现了5G无线网络中高频频段的广泛整合。因此，该项目支持高速率、低延迟的5G技术，这是智能互联社区和万物互联时代所急需的。因此，该项目广泛影响了不断发展的数字社会的方方面面，特别是室内移动应用。此外，该项目在确保妇女和代表性不足的群体参与的同时，提供高度可取的多学科技能方面的劳动力培训。5G无线网络将在未使用的高频频段运行，例如可见光(VL)频段(400-800太赫兹)。虽然它们可以支持超高吞吐量和超低延迟业务需求，但这些频段的无线信道受到衍射能力的限制。这导致与用户移动性的通信链路由于来自静态和/或移动对象的阻塞而频繁中断。初步研究表明，使用一般概率分布模型来描述这些链路中断是不现实的，因为此类中断与环境受限的用户移动性细节有关。因此，经典的优化工具对于5G网络管理将是无效的。另一方面，数据驱动策略可以用于设计智能网络管理策略，该策略可以从环境中学习并自适应地为移动用户分配资源。然而，采用数据驱动的网络管理策略面临以下挑战：1)缺乏室内移动VL信道增益的高质量数据集，2)VL信道增益数据稀疏，这阻碍了传统数据驱动工具的采用。为了解决这些限制，本项目在考虑办公空间布局的同时，开展了以下研究工作：T1)开发高效的5G移动信道模拟器，该模拟器能够反映VL频段的真实时空特征，并捕捉由于对象和用户身体的动态阻塞而导致的链路不可用的影响。该模拟器将公开提供，以支持5G数据驱动的网络管理的进一步研究；T2)开发高效的5G信道预测器，该预测器可为未来时间帧提供有用的VL信道状态信息，尽管信道数据集中的稀疏性很高。Predictor将支持各种主动式数据驱动的5G网络管理战略。本项目中开发的方法和工具将通过模拟基于VL的室内网络设置的试验台进行验证。该奖项反映了NSF的法定使命，并已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。

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Future trends in data traffic require high-quality wireless connections of multi-gigabits per second rates and less than ten milliseconds delay. However, the conventional radio spectrum is quite congested, and hence, it cannot satisfy such high demands. Consequently, unused high-frequency bands will be adopted in the fifth generation and beyond (5G+) wireless networks. Yet, wireless connectivity in high bands is challenged by frequent outages induced by user mobility. Recent studies show that advanced network management techniques based on artificial intelligence can maintain a reliable high-quality link with user mobility. However, to develop such advanced techniques, comprehensive highly-accurate datasets of wireless channel quality are required. Unfortunately, these datasets are not accessible to the research community. The first goal of this project is to develop a realistic and highly-accurate simulator that generates rich datasets of 5G+ wireless channels in the frequency range 400 – 800 Terahertz. This simulator will be made publicly available to empower research efforts in data-driven 5G+ network management solutions. The generated datasets will be validated through a state-of-the-art testbed. Moreover, the generated datasets will be characterized to learn the impact of user mobility patterns on the wireless channel quality. In addition, novel methods will be developed to predict the channel quality due to user mobility, which will further help in developing effective 5G+ network management tools. By empowering future research in data-driven network management solutions, this project enables a broad integration of high-frequency bands in 5G+ wireless networks. As a result, this project supports high-rate low-delay 5G+ technologies, much needed in the era of smart and connected communities and the internet of everything. Thereby, this project broadly impacts myriad aspects of the evolving digital society, particularly, for indoor mobile applications. Furthermore, this project provides workforce training in a highly desirable multi-disciplinary skillset while ensuring the participation of women and underrepresented groups.The 5G+ wireless networks will operate in the unused high-frequency bands, e.g., the visible light (VL) frequency band (400 – 800 Terahertz). While they can support ultra-high throughput and ultra-low latency traffic demands, the wireless channels at such bands suffer from limited diffraction capabilities. This results in frequent outages in communication links with user mobility due to blockage from static and/or mobile objects. Preliminary studies demonstrated that it is not practical to describe these link outages using a general probability distribution model, as such outages are tied to the environment-confined user mobility details. As a result, classical optimization tools will be ineffective for 5G+ network management. On the other hand, data-driven strategies can be used to design intelligent network management strategies that learn from the environment and adaptively allocate resources to the mobile users. However, adopting data-driven network management strategies is challenged by: 1) the absence of high-quality datasets of indoor mobile VL channel gains and 2) the sparsity of the VL channel gain data, which impedes the adoption of conventional data-driven tools. To address these limitations, the proposed project pursues the following research thrusts while considering office room layouts: T1) Development of efficient 5G+ mobile channel simulator that reflects realistic spatio-temporal features in the VL band and captures the impact of link unavailability due to dynamic blockages with the objects and users' bodies. The simulator will be publicly available to empower further research in 5G+ data-driven network management; T2) Development of an efficient 5G+ channel predictor that provides useful VL channel state information for future time frames despite the high sparsity in the channel dataset. The predictor will empower various proactive data-driven 5G+ network management strategies. The developed methods and tools in this project will be validated through a testbed that mimics a VL-based indoor networking setup.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.