SWIFT: Intelligent Spatio-Temporal Metamaterial Massive MIMO Aperture Arrays with Hybrid Learning-based Channel Classifiers for Spectrum-Efficient Secured Wireless Communication

SWIFT：智能时空超材料大规模 MIMO 孔径阵列，具有基于混合学习的信道分类器，可实现频谱高效的安全无线通信

• 批准号: 2229384
• 负责人: Chung-Tse Wu
• 金额: $ 75万
• 依托单位: Rutgers University New Brunswick
• 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 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2229384&HistoricalAwards=false
• 关键词: SWIFT; Intelligent; Spatio; Temporal; Metamaterial

For the next-generation network systems, it is envisioned that billions of mobile and Internet-of-Things (IoT) devices will communicate with each other to provide unprecedented connectivity in a highly smart and secure manner. It is thus of paramount importance to ensure spectrum utilization efficiency as well as physical-layer (PHY) security of mobile and IoT networks against any attack. While spectrum efficiency can be improved by using many antennas in a massive multiple-input multiple-output (MIMO) fashion, the power consumption, hardware complexity and cost will increase drastically as each antenna element requires a dedicated RF transceiver. In parallel, although end-to-end encrypted sessions between the edge devices and the gateway can be used for secure communications, high computational and battery burden of such cryptographic strategies are usually a concern in large-scale systems. To overcome these challenges, this research will harness the spatial dispersion control capability and introduce time modulation for subwavelength metamaterial (MTM) unit cells to create a new intelligent space-time modulated MTM (IST-MTM) antenna aperture, which not only can provide dynamic control of radiation characteristics allowing the optimal spectrum utilization, but also PHY secure transmission for wireless links enabled by directional modulation (DM). Moreover, a new hybrid model-based and learning-based approach (HyPhyLearn) will be incorporated to conduct channel classification even when limited training samples are available. The IST-MTM-based secure communication scheme along with the HyPhyLearn channel classifier will have a profound impact in next-generation wireless networks by providing a highly secured and spectrum-efficient communication scheme, which can be deployed in future 6G networks for smart homes/cities and vehicle-to-vehicle communications. By leveraging outreach activities at Rutgers University, the educational plan of this project aims to broaden participation of graduate, undergraduate and high school students, including underrepresented minority groups in STEM, in relevant research on microwave/antenna technologies, signal processing, machine learning, and wireless communications. Current state-of-the-art antenna systems have treated antennas as fixed radiator hardware, where extensive signal processing is required to achieve the desired specifications, e.g., cryptography-based security or digital beamforming, thereby increasing system cost and power consumption. Such fixed antenna hardware design also hinders the optimal utilization of the spectrum owing to the incapability of dynamically compensating channel effects, thereby reducing the spectral efficiency. Likewise, conventional data-driven classification methods used for authentication need large amount of sampling data to achieve a certain degree of accuracy. To address these issues, the research of IST-MTM MIMO antenna array with hybrid learning-based classifiers aims to achieve the following synergistic outcomes: (1) At the transmitter side, the IST-MTM antenna array will distort the signals towards unintended directions, resulting in a high bit-error-rate (BER) and thus preventing eavesdropping, while preserving the original signals along the directions for authenticated users, and thereby forming a secure communication link. Moreover, the IST-MTM antenna can reconfigure its dispersion characteristics through programming each sub-wavelength MTM unit cell for optimal spectral utilization. (2) At the receiver side, the HyPhyLearn method can conduct classification and authentication even with limited number of training samples to avoid spoofing attacks without the need of using additional spectrum, which is particularly useful for DM, since the transmission link of the IST-MTM array may result in limited training data. (3) In a multipath environment, DM may cause interference for legitimate users, resulting in a degradation in SNR. This multipath effect can be mitigated by using HyPhyLearn to perform accurate classification between desired signals and interference. As such, the unique integration of the IST-MTM array and HyPhyLearn will form a synergistic and complementary effect towards achieving spectrally efficient secured wireless communication networks in the future.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.

对于下一代网络系统，设想数十亿个移动的和物联网（IoT）设备将彼此通信，以高度智能和安全的方式提供前所未有的连接。因此，确保频谱利用效率以及移动的和物联网网络的物理层（PHY）安全性免受任何攻击至关重要。虽然可以通过以大规模多输入多输出（MIMO）方式使用许多天线来提高频谱效率，但是功耗、硬件复杂性和成本将急剧增加，因为每个天线元件需要专用RF收发器。同时，虽然边缘设备和网关之间的端到端加密会话可以用于安全通信，但这种加密策略的高计算和电池负担通常是大规模系统中的一个问题。为了克服这些挑战，本研究将利用空间色散控制能力，并引入亚波长超材料（MTM）单元的时间调制，以创建一种新的智能时空调制MTM（IST-MTM）天线孔径，它不仅可以提供辐射特性的动态控制，从而实现最佳频谱利用率，而且还包括用于通过定向调制（DM）实现的无线链路的PHY安全传输。此外，一个新的混合基于模型和基于学习的方法（HyPhyLearn）将被纳入进行信道分类，即使在有限的训练样本。基于IST-MTM的安全通信方案沿着HyPhyLearn信道分类器将通过提供高度安全和频谱高效的通信方案对下一代无线网络产生深远影响，该方案可以部署在未来的智能家庭/城市和车对车通信的6 G网络中。通过利用罗格斯大学的外联活动，该项目的教育计划旨在扩大研究生，本科生和高中生的参与，包括STEM中代表性不足的少数群体，参与微波/天线技术，信号处理，机器学习和无线通信的相关研究。当前最先进的天线系统已经将天线视为固定的辐射器硬件，其中需要大量的信号处理来实现期望的规格，例如，基于密码的安全性或数字波束成形，从而增加了系统成本和功耗。由于不能动态补偿信道效应，这种固定天线硬件设计也阻碍了频谱的最佳利用，从而降低了频谱效率。同样，用于认证的传统数据驱动分类方法需要大量的采样数据来实现一定程度的准确性。为了解决这些问题，IST-MTM MIMO天线阵列与基于混合学习的分类器的研究旨在实现以下协同成果：（1）在发射机侧，IST-MTM天线阵列将使信号向非预期方向失真，导致高误码率（BER），从而防止窃听，同时为认证用户保留沿着方向的原始信号，从而形成安全通信链路。此外，IST-MTM天线可以通过对每个亚波长MTM单元进行编程来重新配置其色散特性，以实现最佳频谱利用率。(2)在接收器侧，HyPhyLearn方法即使在训练样本数量有限的情况下也可以进行分类和认证，以避免欺骗攻击，而不需要使用额外的频谱，这对于DM特别有用，因为IST-MTM阵列的传输链路可能导致有限的训练数据。(3)在多径环境中，DM可能对合法用户造成干扰，导致SNR下降。这种多径效应可以通过使用HyPhyLearn在期望信号和干扰之间执行准确分类来减轻。因此，IST-MTM阵列和HyPhyLearn的独特集成将形成协同和互补效应，以实现未来频谱效率高的安全无线通信网络。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。

项目成果

Programming Wireless Security Through Learning‐Aided Spatiotemporal Digital Coding Metamaterial Antenna

• DOI: 10.1002/aisy.202300341
• 发表时间: 2022-11
• 期刊: Advanced Intelligent Systems
• 影响因子: 7.4
• 作者: Alireza Nooraiepour;Shaghayegh Vosoughitabar;C. Wu;W. Bajwa;N. Mandayam

Directional Modulation Retrodirective Array-Enabled Physical Layer Secured Transponder for Protected Wireless Data Acquisition

用于受保护的无线数据采集的定向调制反向阵列物理层安全应答器

• DOI: 10.1109/ims37964.2023.10188157
• 发表时间: 2023
• 期刊: 2023 IEEE/MTT-S International Microwave Symposium - IMS 2023
• 作者: Vosoughitabar, Shaghayegh;Nooraiepour, Alireza;Bajwa, Waheed U.;Mandayam, Narayan B.;Wu, Chung-Tse Michael
• 通讯作者: Wu, Chung-Tse Michael