NSF-AoF: Vision-Guided Wireless Communication Systems

NSF-AoF：视觉引导无线通信系统

• 批准号: 2225511
• 负责人: Walid Saad
• 金额: $ 55.5万
• 依托单位: Virginia Polytechnic Institute and State 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: 2022-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2225511&HistoricalAwards=false
• 关键词: NSF; AoF; Vision; Guided; Wireless

With the approach of the golden jubilee of the first mobile phone call made in 1973, it is astonishing to see how far wireless technology has come. More recently, the confluence of computing and communications has transformed wireless devices from merely communication devices into powerful computing and sensing platforms. Enabled by these capabilities and equipped with state-of-the-art sensors, cameras, and other non-radio frequency (non-RF) modalities, modern wireless devices are able to simultaneously perform multiple functions including communications, computing, and imaging. In order to exploit synergies across these functions, this project brings together a synergistic US-Finland team with the goal of laying the fundamental science needed to pioneer a novel paradigm of vision-guided wireless system design using which wireless devices can "view" and map their surrounding wireless environment and its features by fusing heterogeneous multimodal information sensed through their RF and non-RF capabilities. Under this new paradigm, wireless network devices can leverage diverse, sensed information about their environment in order to more effectively communicate and compute. This transformative concept contributes towards boosting the performance of future wireless systems (e.g., 6G) thus paving the way for new wireless applications with tangible societal impact, including advanced extended reality, drones, and connected autonomy. The research is coupled with a suite of collaborative education activities between the US and Finnish partners that involve transfer of ideas, joint tutorials and workshops, outreach events, joint mentoring of students, as well as broad dissemination efforts that help train a workforce skilled in advanced wireless communications and machine learning research.This project develops a novel holistic framework that merges tools from machine learning, distributed optimization, communication theory, and wireless networking to yield key contributions: 1) Systematic approach that merges signal processing techniques with emerging machine learning frameworks in order to fuse heterogeneous information from multiple RF and non-RF modalities for faithfully mapping dynamic wireless environments, 2) New approaches for efficient wireless system design with specific emphasis on new communication strategies for vision-guided networking, 3) A novel framework that advances tools from distributed learning to develop new self-organizing algorithms that can perform cross-layer optimization of wireless functions and resources (e.g., spectrum, power, time) with minimal information exchange, 4) Fundamental analysis of the various properties and tradeoffs involved in the designed learning and optimization algorithms, and 5) Realistic validation of the proposed solutions using a mix of simulation and experimental tools.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.

随着 1973 年第一部移动电话诞生 50 周年的临近，无线技术的进步令人惊叹。最近，计算和通信的融合已将无线设备从单纯的通信设备转变为强大的计算和传感平台。借助这些功能并配备最先进的传感器、摄像头和其他非射频 (non-RF) 模式，现代无线设备能够同时执行多种功能，包括通信、计算和成像。为了利用这些功能之间的协同作用，该项目汇集了一支具有协同作用的美国-芬兰团队，其目标是奠定开创视觉引导无线系统设计的新颖范式所需的基础科学，无线设备可以通过融合通过其射频和非射频功能感测到的异构多模态信息来“查看”和映射其周围的无线环境及其特征。在这种新范式下，无线网络设备可以利用有关其环境的各种感知信息，以便更有效地进行通信和计算。这一变革性概念有助于提高未来无线系统（例如 6G）的性能，从而为具有切实社会影响的新无线应用铺平道路，包括先进的扩展现实、无人机和互联自治。该研究与美国和芬兰合作伙伴之间的一系列合作教育活动相结合，包括思想交流、联合教程和研讨会、外展活动、学生联合指导以及帮助培训高级无线通信和机器学习研究技能的劳动力的广泛传播工作。该项目开发了一个新颖的整体框架，融合了机器学习、分布式优化、通信理论和无线网络等工具，以产生关键的成果。 贡献：1) 将信号处理技术与新兴机器学习框架相结合的系统方法，以融合来自多种射频和非射频模态的异构信息，从而忠实地映射动态无线环境，2) 高效无线系统设计的新方法，特别强调视觉引导网络的新通信策略，3) 一种新颖的框架，该框架将分布式学习的工具改进为开发新的自组织算法，该算法可以 以最少的信息交换执行无线功能和资源（例如频谱、功率、时间）的跨层优化，4) 对设计的学习和优化算法中涉及的各种属性和权衡进行基本分析，以及 5) 使用模拟和实验工具的组合对所提出的解决方案进行现实验证。该奖项反映了 NSF 的法定使命，并通过使用基金会的评估进行评估，被认为值得支持。 智力价值和更广泛的影响审查标准。

项目成果

A Probabilistic Reformulation Technique for Discrete RIS Optimization in Wireless Systems

• DOI: 10.1109/twc.2023.3326091
• 发表时间: 2023-03
• 期刊: IEEE Transactions on Wireless Communications
• 影响因子: 10.4
• 作者: Anish Pradhan;Harpreet S. Dhillon

Benchmarking of Anomaly Detection Techniques in O-RAN for Handover Optimization

用于切换优化的 O-RAN 异常检测技术基准测试

• DOI: 10.1109/iwcmc58020.2023.10183347
• 发表时间: 2023
• 期刊: IEEE
• 作者: Mahrez, Zineb;Driss, Maryam Ben;Sabir, Essaid;Saad, Walid;Driouch, Elmahdi
• 通讯作者: Driouch, Elmahdi

Stochastic Geometry Analysis of Localizability in Vision-Based Geolocation Systems

• DOI: 10.1109/ieeeconf59524.2023.10477066
• 发表时间: 2023-10
• 期刊: 2023 57th Asilomar Conference on Signals, Systems, and Computers
• 作者: Student Member Ieee Haozhou Hu;F. I. Harpreet S. Dhillon;F. I. R. Michael Buehrer

Statistically Optimal Beamforming and Ergodic Capacity for RIS-Aided MISO Systems

• DOI: 10.1109/access.2023.3347925
• 发表时间: 2023-07
• 期刊: IEEE Access
• 影响因子: 3.9
• 作者: Kali Krishna Kota;I. M. S. S. M. Student Member-I.-M.-S.-S.-M.-Student-Member-2280938857;I. P. D. M. Student Member;I. H. S. D. Member;Kali Krishna Kota

Novel Probabilistic Reformulation Technique for Unconstrained Discrete RIS Optimization

• DOI: 10.1109/pimrc56721.2023.10293983
• 发表时间: 2023-09
• 期刊: 2023 IEEE 34th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)
• 作者: Anish Pradhan;Harpreet S. Dhillon