RUI: SpecEES: Collaborative Research: Enabling Secure, Energy-Efficient, and Smart In-Band Full Duplex Wireless

RUI：SpecEES：协作研究：实现安全、节能和智能的带内全双工无线

基本信息

批准号：
2300955
负责人：
Shaoen Wu
金额：
$ 25万
依托单位：
Kennesaw State University Research and Service Foundation
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 2024-12-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2300955&HistoricalAwards=false
关键词：
RUI SpecEES Collaborative Research Enabling

项目摘要

In-band full-duplex (IBFD) wireless communication technique has tremendous potentials in spectral efficiency because of its simultaneous transmission and reception of information. Although IBFD wireless communication technique has been theoretically investigated and analyzed for years, it remains very challenging to be systemically enabled in practice because of a few hurdles. This project will design and develop deep learning resolutions of self-interference cancellation, power control, and security for future IBFD wireless communication systems. The research can potentially double the wireless spectrum efficiency and impact future wireless standards and policies. Outcomes as publications and open source codes will be made available to the research community to significantly facilitate the research on deep learning-based wireless communications. This project will integrate the research outcomes into course curricula to promote training workforce with knowledge and skills in deep learning and future wireless system design. Underrepresented students will be recruited to participate as research assistants or through special programs, e.g., the Louis Stokes Alliance for Minority Participation Program or the Sloan Engineering Program at the collaborative institutions. This research tackles three major challenges and problems to enable secure, spectrum-efficient, and energy-efficient IBFD wireless communication systems. First, this project will design deep learning based all-digital self-interference cancellation solutions with the potential of doubling the spectrum efficiency. Such design with nonlinear solutions is expected to model the self-interference much more accurately than conventional solutions. The proposed per-symbol estimation of wireless channel condition will provide the highest resolution of channel dynamics to upper layers for cross-layer designs. Second, deep learning power control solutions will be designed to maximize the energy efficiency of IBFD wireless system. These solutions are expected to achieve optimal performance while overcoming the computational and mathematical hurdles in traditional solutions. Third, by data-mining the IBFD channel dynamics, new solutions for wireless security with high degrees of efficiency and secrecy will be developed for IBFD wireless communication systems and networks.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.

带内无线电通信技术在波段中具有巨大的频谱效率潜力，因为它同时传输和接收信息。尽管理论上对IBFD无线通信技术进行了多年的研究和分析，但由于一些障碍，在实践中可以系统地启用它仍然非常具有挑战性。该项目将设计和制定深度学习的解决方案，以实现自我干扰取消，功率控制和安全性IBFD无线通信系统的安全性。这项研究可能会使无线频谱效率增加一倍，并影响未来的无线标准和政策。作为出版物和开源代码的结果将提供给研究社区，以显着促进有关基于深度学习的无线通信的研究。该项目将将研究成果整合到课程课程中，以促进培训员工以及深度学习和未来无线系统设计方面的知识和技能。代表性不足的学生将被招募为研究助理或特殊计划，例如，路易斯·斯托克斯（Louis Stokes）的少数民族参与计划或协作机构的斯隆工程计划。这项研究解决了三个主要的挑战和问题，以实现安全，频谱和节能的IBFD无线通信系统。首先，该项目将设计基于深度学习的全数字自我干扰取消解决方案，并具有使频谱效率加倍的潜力。这种使用非线性解决方案的设计将比传统解决方案更准确地建模。提出的无线通道条件的每符号估计将为跨层设计提供最高的通道动力学分辨率。其次，深度学习能力控制解决方案将旨在最大化IBFD无线系统的能源效率。这些解决方案有望在克服传统解决方案中的计算和数学障碍的同时实现最佳性能。第三，通过数据挖掘IBFD通道动力学，将为IBFD无线通信系统和网络开发针对高效率和保密性的无线安全解决方案。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的智力功能和更广泛影响的评估来进行评估的审查审查Criteria通过评估的支持。