NSF-AoF: Vision-Guided Wireless Communication Systems

随着1973年第一个移动的电话问世50周年纪念日的临近，无线技术的发展令人惊讶。最近，计算和通信的融合已经将无线设备从仅仅是通信设备转变为强大的计算和感测平台。通过这些功能并配备最先进的传感器、相机和其他非射频（非RF）模态，现代无线设备能够同时执行包括通信、计算和成像在内的多种功能。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需要开拓一个新的范式的视觉引导的无线系统设计，使用该无线设备可以“查看”和映射他们周围的无线环境和它的功能，通过融合异构多模态信息，通过他们的RF和非RF功能感测。在这种新模式下，无线网络设备可以利用关于其环境的各种感知信息，以便更有效地进行通信和计算。这种变革性的概念有助于提高未来无线系统的性能（例如，6 G），从而为具有切实社会影响的新无线应用铺平了道路，包括先进的延展实境、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，包括思想转移，联合教程和研讨会，外展活动，联合指导学生，以及广泛的传播工作，帮助培训掌握先进无线通信和机器学习研究的劳动力。该项目开发了一种新颖的整体框架，将机器学习工具，分布式优化，通信理论和无线网络，以产生关键贡献：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，以便融合来自多个RF和非RF模态的异构信息，以忠实地映射动态无线环境，2）用于高效无线系统设计的新方法，特别强调用于视觉引导联网的新通信策略，3）一个新的框架，从分布式学习的工具，以开发新的自组织算法，可以执行跨无线功能和资源的层优化（例如，频谱、功率、时间），4）对所设计的学习和优化算法中涉及的各种属性和权衡的基本分析，（五）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智力价值和更广泛的影响进行评估，被认为值得支持审查标准。

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.