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年第一个手机电话的黄金禧年的接近，令人惊讶的是，看到无线技术已经发生了多远。最近，计算和通信的汇合将无线设备从仅仅是通信设备转换为强大的计算和传感平台。由这些功能启用，并配备了最先进的传感器，相机和其他非Radio频率（非RF）模式，现代无线设备能够同时执行多个功能，包括通信，计算和成像。 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.在这个新的范式下，无线网络设备可以利用多样化的，感知有关其环境的信息，以便更有效地进行沟通和计算。这种变革性的概念有助于提高未来无线系统的性能（例如6G），从而为具有切实的社会影响的新无线应用铺平了道路，包括高级扩展现实，无人机和连接的自主权。这项研究与美国与芬兰合作伙伴之间的一系列协作教育活动相结合，这些活动涉及转移思想，联合教程和研讨会，宣传活动，宣传活动，共同的指导，共同指导学生以及有助于培训型型劳动力的广泛传播工作，这些劳动力熟练熟练的无线沟通和机器学习的工具，从而开发了一项新型的沟通能力。贡献：1）将信号处理技术与新兴的机器学习框架合并的系统方法，以融合来自多个RF和非RF模式的异质信息，以忠实地绘制动态无线环境的忠实绘制动态无线环境，2）有效的无线系统设计的新方法，可以将特定的沟通策略与新型框架进行分配，以从新颖的策略中，以创建新型工具，3）从3）开发出来的工具3）对无线功能和资源（例如，频谱，力量，时间）进行跨层优化，并使用最小的信息交换，4）对设计和优化算法所涉及的各种属性和权衡的基本分析，以及5）5）使用仿真和实验工具的组合，对拟议的解决方案进行了现实验证。智力优点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

Landmark-Based Localization Using Range Measurements: A Stochastic Geometry Perspective

使用范围测量进行基于地标的定位：随机几何视角

• 发表时间: 2023
• 期刊: IEEE/IFIP WiOpt Workshops
• 作者: Hu, Haozhou;Dhillon, Harpreet S.;Buehrer, R. Michael
• 通讯作者: Buehrer, R. Michael