Collaborative Research: NSF-AoF: CIF: Small: AI-assisted Waveform and Beamforming Design for Integrated Sensing and Communication

通信和传感传统上是独立运行的，使用不同的频段和硬件。这些技术的集成被称为集成传感和通信（ISAC），旨在提高频谱、能源和成本效率。ISAC的设计在无线通信、定位、自动驾驶汽车、区域成像、环境监测等领域都有应用。然而，仅通过分析方法实现最佳ISAC设计面临着性能权衡和复杂性的挑战。为了克服这些障碍，该项目采用人工智能（AI）来设计这些解决方案，并为ISAC建立理论基础。一个独特的欧洲合作（芬兰）联合了不同的研究专业知识，促进了经济、社会和研究的影响。知识转移是通过传播、课程开发、学生交流和行业合作实现的。该项目还与农贸市场合作，展示ISAC造福当地农业的潜力。该项目旨在通过人工智能辅助ISAC设计探索传感和通信的最佳集成。这项研究包括三个相互关联的重点。第一个重点是为传感和通信设计统一的波形、星座和波束形成技术。此外，将开发基于人工智能的渠道学习和生成方法。第二个重点是使用大孔径阵列实现ISAC设计。本项目旨在建立电大孔径阵列ISAC通道扩展近场的理论基础，并利用其独特的通道特性优化集成增益。第三个重点是致力于实时实施和评估人工智能辅助ISAC设计和算法，考虑性能、鲁棒性和先进指标等因素。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。

Communications and sensing have traditionally operated independently, utilizing separate frequency bands and hardware. The integration of these technologies, known as integrated sensing and communication (ISAC), aims to enhance spectral, energy, and cost efficiency. ISAC designs find applications in wireless communications, localization, autonomous vehicles, area imaging, environmental monitoring, and more. However, achieving optimal ISAC designs solely through analytical approaches faces challenges due to performance trade-offs and complexity. To overcome these hurdles, this project employs artificial intelligence (AI) to design such solutions and establish theoretical foundations for ISAC. A unique European collaboration (Finland) unites diverse research expertise, fostering economic, societal, and research impacts. Knowledge transfer occurs through dissemination, curriculum development, student exchanges, and industry collaborations. The project also engages with farmers' markets to showcase ISAC's potential to benefit local agriculture. This project aims to explore the optimal integration of sensing and communications through AI-assisted ISAC designs. The research consists of three interconnected thrusts. The first thrust involves designing unified waveforms, constellations, and beamforming techniques for both sensing and communications. Additionally, AI-based channel learning and generation methods will be developed. The second thrust focuses on implementing ISAC designs using large aperture arrays. The project aims to establish the theoretical foundations of the extended near-field of ISAC channels with electrically large aperture arrays and leverage the unique channel characteristics to optimize integration gains. The third thrust is dedicated to real-time implementation and evaluation of the AI-assisted ISAC designs and algorithms, considering factors such as performance, robustness, and advancement metrics.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.