NSF-AoF: Vision-Guided Wireless Communication Systems

随着1973年第一个移动电话诞生50周年纪念的临近，无线技术的发展令人惊讶。最近，计算和通信的融合使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。通过这些功能，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，该项目汇集了一个协同的美国-芬兰团队，目标是奠定基础科学，以开拓视觉引导无线系统设计的新范式，无线设备可以通过融合通过射频和非射频功能感知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。在这种新模式下，无线网络设备可以利用有关其环境的各种感测信息，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，从而为具有实际社会影响的新型无线应用铺平道路，包括先进的扩展现实，无人机和连接自治。这项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，包括思想转移、联合教程和研讨会、外展活动、学生联合指导，以及广泛的传播工作，以帮助培训熟练掌握先进无线通信和机器学习研究的劳动力。该项目开发了一个新的整体框架，融合了机器学习、分布式优化、通信理论和无线网络等工具，产生了以下关键贡献：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式的异构信息，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)一个新的框架，将分布式学习的工具推进到开发新的自组织算法，该算法可以以最小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)对所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；5)使用模拟和实验工具的混合对所提出的解决方案进行现实验证。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.