CIF: Small: NSF-DST: Zak-OTFS - How to Make Communication and Radar Sensing More Predictable in 6G

在过去的三十年中，无线应用已经从语音通信发展到高速数据，再发展到集成的传感和通信，并且传播环境已经发展到包括非地面和地面网络。无线信号处理的标准方法基于数学模型，但随着应用和环境的发展，多普勒扩展开始以千赫兹为单位进行测量，标准方法开始崩溃。另一方面，在过去的十年里，机器学习已经彻底改变了图像和自然语言处理，在未来的十年里，它有望改变无线信号处理。当底层数据流是可预测的时，机器学习算法是最有效的，这个项目的重点是设计无线输入输出关系，使其是可预测的，并且它只会缓慢地变化，也就是说，与无线环境的物理速度相同。该项目将开展必要的基础研究，使无线信号处理在延迟多普勒域。该研究计划分为三个相互关联的研究方向：1）设计调制方案，最大限度地减少预测无线输入输出关系的复杂性; 2）设计离散延迟多普勒域中的滤波器，实现集成传感和通信; 3）设计新的波形，优化传感性能。预期成果包括无模型操作的可能性，这可以在需要数学模型的传统模型依赖模式无法实现时实现通信。印度和美国之间的国际合作将扩大项目的影响，软件和硬件测试平台将把理论转化为实践。该奖项反映了NSF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。

Over the last thirty years wireless applications have evolved from voice communication to high-speed data to integrated sensing and communications, and propagation environments have evolved to include both non-terrestrial and terrestrial networks. The standard approach to wireless signal processing is based on mathematical models, but as applications and environments evolve, Doppler spreads start to be measured in kilohertz, and the standard approach starts to break down. On the other hand, over the last ten years, machine learning has revolutionized image and natural language processing, and over the next ten years, it is expected to transform wireless signal processing. Machine learning algorithms are most effective when the underlying data stream is predictable, and this project is focused on engineering the wireless input-output relation so that it is predictable, and so that it only changes slowly, that is, at the same speed as the physics of the wireless environment. This project will develop the fundamental research necessary to enable wireless signal processing in the delay-Doppler domain. The research program is organized into three interconnected research thrusts: 1) design of modulation schemes that minimize the complexity of predicting the wireless input-output relation; 2) design of filters in the discrete delay-Doppler domain that enable integrated sensing and communication; and 3) design of new waveforms that optimize sensing performance. Expected outcomes include the possibility of model-free operation, which can enable communication when traditional model-dependent modes requiring mathematical models are out of reach. International collaboration between India and the US will broaden the project impact, and software and hardware testbeds will translate theory into practice.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.