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.

在基于网络的数据分析和人工智能(AI)时代，先进的无线网络和技术正在占据中心舞台。面对广泛的人工智能应用对高速无线数据连接的需求激增，宽带无线网络中有效的频谱利用和共存的有效解决方案变得越来越重要。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，以高效和准确地评估无线信道条件和覆盖质量图(Radiomap)，以便为数量庞大的智能手机和其他无线设备提供高质量的服务。该项目的成果可以为高速无线服务的部署及其在网络人工智能应用中的广泛应用做出重大贡献。这项研究还将通过为K-12、女性和代表性不足的少数族裔学生提供STEM机会，通过许多教育机会产生更广泛的影响。在无线网络中，准确的前向链路RF信道估计对于实现移动终端的高速数据服务至关重要。同时，复杂覆盖环境中射频信号效应的无线电映射为协调网络服务站高效利用有限的射频资源并在保护区域内实现有效的干扰抑制提供了重要信息。该项目旨在为广域无线网络开发新的实用算法和资源管理工具，以有效和准确地评估无线电信道条件和干扰无线电地图，以促进与受保护的射频应用程序共存，服务于潜在的无源用户。具体地说，研究小组将利用机器学习和数据分析在广泛的工程和科学应用中的最新成功，开发基于学习的创新算法，通过最大限度地减少宝贵资源的消耗，同时提高无线质量和频谱利用率，实现准确的射频信道估计。为了同时保护共存的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获得准确的无线电地图估计。此外，研究小组将广泛调查频谱分配和动态链路自适应对干扰缓解的影响，以保护指定区域的敏感服务。这一奖项反映了NSF的法定使命，并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。

项目成果

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

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

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

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

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