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，可以预见，数十亿个移动设备（IoT）设备将相互通信，以高度聪明且安全的方式提供前所未有的连接。因此，至关重要的是要确保频谱利用效率以及移动和物联网网络的物理层（PHY）安全性，以防止任何攻击。虽然可以通过在大量的多输入多输出（MIMO）时尚中使用许多天线来提高频谱效率，但由于每个天线元素都需要专用的RF收发器，功耗，硬件复杂性和成本将大大增加。同时，尽管边缘设备和网关之间的端到端加密会话可用于安全通信，但此类加密策略的高计算和电池负担通常在大型系统中是一个问题。为了克服这些挑战，这项研究将利用空间分散控制能力，并引入亚电波超材料（MTM）单位单元单元的时间调节，以创建新的智能时空调制MTM（IST-MTM）天线孔径，不仅可以提供动态控制辐射特性，允许最佳光谱利用，还可以通过定向调制（DM）实现无线链接的PHY安全传输。此外，即使有有限的训练样本，也将合并一种新的基于混合模型和基于学习的方法（菌丝）来进行渠道分类。基于IST-MTM的安全通信方案以及杂项频道分类器将通过提供高度安全且频谱高效的通信方案来对下一代无线网络产生深远的影响，该方案可以在未来的6G网络中用于智能家庭//城市和车辆到车辆通信。通过利用罗格斯大学的外展活动，该项目的教育计划旨在扩大研究生，本科和高中生的参与，包括STEM中代表性不足的少数群体，在有关微波/天线技术，信号处理，机器学习和机器学习和机器学习和机器学习和机器学习和机器学习和机器学习和机器学习和机器的相关研究中无线通信。当前的最新天线系统已将天线视为固定的散热器硬件，在该硬件中，需要广泛的信号处理才能实现所需的规格，例如基于加密的安全性或数字波束形成，从而增加了系统成本和功耗。这种固定的天线硬件设计还阻碍了由于动态补偿通道效应的无能，因此降低了光谱效率，因此妨碍了光谱的最佳利用。同样，用于身份验证的常规数据驱动分类方法需要大量的采样数据才能达到一定程度的准确性。为了解决这些问题，具有基于混合学习的分类器的IST-MTM MIMO天线阵列的研究旨在实现以下协同成果：（1）在发射器侧，IST-MTM天线阵列将使信号扭曲信号，向意外指示，无意识的指示，，，，，无意识地指示，导致较高的位率率（BER），从而防止窃听，同时为经过身份验证的用户保留原始信号，从而形成安全的通信链接。此外，IST-MTM天线可以通过编程每个亚波长MTM单位电池来重新配置其分散特性，以进行最佳光谱利用。 （2）在接收器侧，即使使用有限数量的训练样品，杂项方法也可以进行分类和身份验证，以避免欺骗攻击而无需使用其他光谱，这对于DM特别有用，因为IST-的传输链接MTM阵列可能会导致培训数据有限。 （3）在多路径环境中，DM可能会导致合法用户的干扰，从而导致SNR降解。可以通过使用杂种素来在所需的信号和干扰之间进行准确的分类来减轻这种多径效应。因此，IST-MTM阵列和杂项的独特整合将形成协同和互补的效果，以实现未来实现光谱有效的无线通信网络。该奖项反映了NSF的法定任务，并被认为是通过使用评估的支持值得的。基金会的智力优点和更广泛的影响评论标准。

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