SWIFT:SMALL: Dynamic Wireless Resource Management and Transceiver Adaptation for Efficient Spectrum Utilization and Coexistence

SWIFT:SMALL：动态无线资源管理和收发器适配，实现高效频谱利用和共存

• 批准号: 2029027
• 负责人: Zhi Ding
• 金额: $ 41.28万
• 依托单位: University of California-Davis
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-15 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2029027&HistoricalAwards=false
• 关键词: SWIFT; SMALL; Dynamic; Wireless; Resource

Advanced wireless networks and technologies are taking the center stage in the era of cyber-based data analytics and artificial intelligence (AI). Faced with the surging needs for high speed wireless data connections driven by widespread AI applications, effective solutions for effective spectrum utilization and coexistence in broadband wireless networks become increasingly critical. This project aims to develop new estimation methods and resource management tools for wide-area radio networks to efficiently and accurately assess the radio channel conditions and coverage quality map (radiomap) for providing high quality services to the huge number of smartphones and other wireless devices. The outcomes of this project can contribute significantly to the deployment of high speed wireless services and their broadening applications in networked AI applications. The broader impact from this research will also come through many educational opportunities by providing opportunities in STEM to K-12, women, and under-represented minority students.In wireless networks, accurate forward link RF channel estimation is critical to achieving high speed data services to mobile terminals. At the same time, radiomap of RF signal effect in a complex coverage environment provides vital information to coordinate network service stations to efficient utilize limited RF resources and to achieve effective interference mitigation in protected regions. This project aims to develop new practical algorithms and resource management tools for wide-area radio networks to efficiently and accurately assess the radio channel conditions and interference radiomap, to facilitate coexistence with protected RF applications serving potentially passive users. Specifically, the research group will leverage recent successes of machine learning and data analytics across a wide range of engineering and scientific applications to develop innovative learning-based algorithms for accurate RF channel estimation by minimizing the consumption of valuable resources while improving wireless quality and spectrum utilization. To simultaneously protect sensitive nodes of coexisting wireless applications, the project utilizes low cost and distributed sensor measurements to derive accurate radiomap estimation. Furthermore, the research group will extensively investigate the impact of spectrum allocation and dynamic link-adaptation on interference mitigation to protect sensitive services in designated areas.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.

高级无线网络和技术正在基于网络数据分析和人工智能（AI）时代处于中心阶段。面对由广泛的AI应用程序驱动的高速无线数据连接的需求，有效频谱利用的有效解决方案和宽带无线网络共存的解决方案变得越来越关键。该项目旨在为广域无线网络开发新的估计方法和资源管理工具，以有效，准确地评估无线电通道条件和覆盖质量图（RadiOmap），以为大量智能手机和其他无线设备提供高质量的服务。该项目的结果可以为高速无线服务的部署及其在网络AI应用程序中的扩展应用做出重大贡献。这项研究的更广泛影响还将通过许多教育机会来通过为K-12，妇女和代表性不足的少数族裔学生提供机会。同时，在复杂的覆盖范围环境中，RF信号效应的放射线图为协调网络服务站提供了重要信息，以有效利用有限的RF资源并实现受保护区域的有效干扰缓解。该项目旨在为广域无线电网络开发新的实用算法和资源管理工具，以有效，准确地评估无线电渠道条件和干扰放射线瘤，以促进与为潜在被动用户服务的受保护的RF应用程序的共存。具体而言，研究小组将利用广泛的工程和科学应用程序的机器学习和数据分析的最新成功来开发基于创新的基于学习的算法，以通过最大程度地减少有价值的资源的消耗，同时改善无线质量和光谱利用，从而准确地估算RF渠道估计。为了同时保护共存无线应用的敏感节点，该项目利用低成本和分布式传感器测量来得出准确的放射线瘤估计。此外，研究小组将广泛研究频谱分配和动态链接适应对干扰缓解措施的影响，以保护指定领域的敏感服务。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛的影响审查标准来通过评估来通过评估来支持的。

项目成果

Spectral Clustering Aided User Grouping and Scheduling in Wideband MU-MIMO Systems

• DOI: 10.1109/icc45041.2023.10279388
• 发表时间: 2023-05
• 期刊: ICC 2023 - IEEE International Conference on Communications
• 作者: Chih-Ho Hsu;Carlos Feres;Zhi Ding

OTFS Signaling for SCMA With Coordinated Multi-Point Vehicle Communications

• DOI: 10.1109/tvt.2023.3248119
• 发表时间: 2023-02
• 期刊: IEEE Transactions on Vehicular Technology
• 影响因子: 6.8
• 作者: Yao Ge;Qinwen Deng;David González G.;Y. Guan;Z. Ding

End-to-End Image Classification and Compression With Variational Autoencoders

• DOI: 10.1109/jiot.2022.3182313
• 发表时间: 2022-11
• 期刊: IEEE Internet of Things Journal
• 影响因子: 10.6
• 作者: Lahiru D. Chamain;Siyu Qi;Zhi Ding

Over-the-Air Multisensor Collaboration for Resource Efficient Joint Detection

• DOI: 10.1109/tsp.2023.3310895
• 发表时间: 2024
• 期刊: IEEE Transactions on Signal Processing
• 影响因子: 5.4
• 作者: Carlos Feres;B. Levy;Zhi Ding

Receiver Design for OTFS with a Fractionally Spaced Sampling Approach

• DOI: 10.1109/twc.2021.3055585
• 发表时间: 2021-07-01
• 期刊: IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
• 影响因子: 10.4
• 作者: Ge, Yao;Deng, Qinwen;Ding, Zhi
• 通讯作者: Ding, Zhi