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：
1923717
负责人：
Shiwen Mao
金额：
$ 25万
依托单位：
Auburn University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-01 至 2024-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1923717&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.

带内全双工（IBFD）无线通信技术由于能够同时传输和接收信息，因此在频谱效率方面具有巨大的潜力。尽管IBFD无线通信技术已经在理论上进行了多年的研究和分析，但由于存在一些障碍，在实践中系统地实现仍然非常具有挑战性。该项目将为未来IBFD无线通信系统设计和开发自干扰消除、功率控制和安全性的深度学习解决方案。该研究有可能使无线频谱效率提高一倍，并影响未来的无线标准和政策。成果将作为出版物和开源代码提供给研究界，以极大地促进基于深度学习的无线通信的研究。该项目将把研究成果整合到课程中，以促进培训劳动力掌握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作为研究助理或通过特殊项目参与，例如路易斯斯托克斯少数族裔参与计划联盟或合作机构的斯隆工程项目。这项研究解决了三个主要挑战和问题，以实现安全、频谱高效和节能的 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干扰消除解决方案，具有使频谱效率翻倍的潜力。这种采用非线性解决方案的设计有望比传统解决方案更准确地模拟自干扰。所提出的无线信道条件的每符号估计将为跨层设计的上层提供最高分辨率的信道动态。其次，将设计深度学习功率控制解决方案，以最大限度地提高IBFD无线系统的能源效率。这些解决方案预计将实现最佳性能，同时克服传统解决方案中的计算和数学障碍。第三，通过对 IBFD 信道动态进行数据挖掘，将为 IBFD 无线通信系统和网络开发高效、保密的新无线安全解决方案。该奖项反映了 NSF 的法定使命，并通过使用评估结果被认为值得支持。基金会的智力价值和更广泛的影响审查标准。

项目成果

期刊论文数量（20）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Quantum Machine Learning: Recent Advances and Outlook

DOI：
10.1109/mwc.001.1900341
发表时间：
2020-06-01
期刊：
IEEE WIRELESS COMMUNICATIONS
影响因子：
12.9
作者：
O'Quinn, Wesley;Mao, Shiwen
通讯作者：
Mao, Shiwen

A Joint Learning and Game-Theoretic Approach to Multi-Dimensional Resource Management in Fog Radio Access Networks

DOI：
10.1109/tvt.2022.3214075
发表时间：
2023-02
期刊：
IEEE Transactions on Vehicular Technology
影响因子：
6.8
作者：
Yaohua Sun;Siqi Chen;Zeyu Wang;S. Mao
通讯作者：
Yaohua Sun;Siqi Chen;Zeyu Wang;S. Mao

Online Distributed Offloading and Computing Resource Management With Energy Harvesting for Heterogeneous MEC-Enabled IoT

DOI：
10.1109/twc.2021.3076201
发表时间：
2021-10-01
期刊：
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
影响因子：
10.4
作者：
Xia, Shichao;Yao, Zhixiu;Mao, Shiwen
通讯作者：
Mao, Shiwen

Dealing with Link Blockage in mmWave Networks: A Combination of D2D Relaying, Multi-beam Reflection, and Handover

DOI：
10.1109/twc.2022.3152472
发表时间：
2022
期刊：
IEEE Transactions on Wireless Communications
影响因子：
10.4
作者：
Mingjie Feng;S. Mao;Tao Jiang
通讯作者：
Mingjie Feng;S. Mao;Tao Jiang

Beamforming Design for Rate Splitting MIMO

DOI：
10.1109/iccworkshops50388.2021.9473811
发表时间：
2021-06
期刊：
2021 IEEE International Conference on Communications Workshops (ICC Workshops)
影响因子：
0
作者：
Ticao Zhang;Shiwen Mao
通讯作者：
Ticao Zhang;Shiwen Mao