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

ERI：在室内 5G 无线网络中实现数据驱动的资源管理

• 批准号: 2138234
• 负责人: Muhammad Ismail
• 金额: $ 19.95万
• 依托单位: Tennessee Technological University
• 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: 2022-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2138234&HistoricalAwards=false
• 关键词: ERI; Empowering; Data; Driven; Resource

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.

该奖项的全部或部分资金来源于《2021 年美国救援计划法案》（公法 117-2）。数据流量的未来趋势需要每秒数千兆位速率和小于 10 毫秒延迟的高质量无线连接。然而，传统的无线电频谱非常拥挤，因此无法满足如此高的要求，第五代及以后的无线网络将采用未使用的高频频段。最近的研究表明，基于人工智能的先进网络管理技术可以与用户移动性保持可靠的高质量链路，但是，要开发这种先进的技术，需要全面准确的技术。不幸的是，研究界无法访问这些数据集，该项目的首要目标是开发一个真实且准确的模拟器，以生成频率范围 400 的丰富 5G+ 无线信道数据集。 800 太赫兹。该模拟器将公开，以支持数据驱动的 5G+ 网络管理解决方案的研究工作。此外，生成的数据集将通过最先进的测试平台进行验证。此外，还将开发新的方法来预测用户移动性造成的信道质量，这将进一步有助于开发有效的 5G+ 网络管理工具。该项目通过数据驱动的网络管理解决方案，实现了5G+无线网络中高频频段的广泛集成，因此，该项目支持智能互联社区和互联时代急需的高速率低延迟5G+技术。因此，该项目广泛影响不断发展的数字社会的各个方面，特别是室内移动应用。此外，该项目提供了非常理想的多学科技能组合的劳动力培训，同时确保女性和代表性不足的群体的参与。 。这5G+ 无线网络将在未使用的高频段运行，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流量需求，但无线信道仍可满足这些需求。这些频段的绕射能力有限，这表明由于静态和/或移动物体的阻塞，导致用户移动性的通信链路频繁中断。初步研究表明，描述这些链路中断是不切实际的。使用通用概率分布模型，因为此类中断与环境限制的用户移动细节相关，因此传统的优化工具对于 5G+ 网络管理将无效。另一方面，可以使用数据驱动的策略进行设计。然而，采用数据驱动的网络管理策略面临着以下挑战：1) 缺乏室内移动 VL 信道增益的高质量数据集；2) 缺乏室内移动 VL 信道增益。的稀疏性VL 信道增益数据阻碍了传统数据驱动工具的采用。 为了解决这些限制，拟议项目在考虑办公室布局的同时追求以下研究重点： T1) 开发真实时空的高效 5G+ 移动信道模拟器。该模拟器将公开用于 5G+ 数据驱动网络的进一步研究。 T2) 开发 5G+ 信道预测器，尽管信道数据集高度稀疏，但仍可提供有用的 VL 信道状态信息，从而支持各种主动数据驱动的高效 5G+ 网络管理策略。该项目中的项目将通过模仿基于 VL 的室内网络设置的测试台进行验证。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。