GOALI: SpecEES: Collaborative Research: Lens Antenna Subarrays and 3D Hardware Integration for Energy Efficient and High-Data Rate Mm-Wave Wireless Networks

目标：SpecEES：协作研究：用于节能和高数据速率毫米波无线网络的透镜天线子阵列和 3D 硬件集成

• 批准号: 1923884
• 负责人: Arun Natarajan
• 金额: $ 25万
• 依托单位: Oregon State 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: 2019-09-15 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1923884&HistoricalAwards=false
• 关键词: GOALI; SpecEES; Collaborative; Research; Lens

The increasing demand for wireless data has led to interest in wireless communication at mm-wave frequency bands where a large amount of spectrum is available, thus enabling high data rates for next generation wireless networks. However, conventional mm-wave links require high power transmitters, making transmitter efficiency critical. Additionally, these links must support multiple users at the same time. This project will develop both energy and spectrum efficient transmitters and receivers operating at mm-wave frequencies with innovation at all layers of the wireless link from communication protocols to transmitter/receiver integration circuits and antenna/lens design. In addition, advanced 3D printing techniques for low-cost manufacturing of mm-wave arrays will be studied. From the technology perspective, the proposed mm-wave network architectures offering high bandwidth, low latency and low-cost communications solutions will create more high-tech jobs and have major economic impact. The educational impact of the project includes curriculum enhancement, graduate course development, and research training for graduate students which also includes an emphasis on professional development and research management. The project will also expand research opportunities for high-school students and students from underrepresented groups, creating and expanding the pipeline of STEM students. A strong collaboration with industry partners will improve dissemination of the technology advances along with important training opportunities for students working on the project.Massive antenna arrays, with hundreds of elements, capable of high gain and multiple-input multiple-output (MIMO)/multi-beamforming are attractive for multi-user wireless links at mm-wave frequencies. However, achieving such MIMO operation through digital beamforming is prohibitive due to costly and power-hungry mm-wave signal chains, analog-to-digital and digital-to-analog converters required for each antenna element. As a solution, hybrid MIMO architectures with reduced number of mm-wave signal chains have recently attracted interest for practical realizations of multiple MIMO stream transmissions. However, these architectures still exhibit drawbacks in terms of spectrum and energy efficiency and do not address hardware complexity issues. This project aims to address fundamental challenges in energy efficiency, spectrum efficiency, and hardware complexity in large mm-wave arrays through a lens antenna subarray (LAS) approach. The research plan is based on an end-to-end investigation that includes antenna array designs within the LAS scheme, mm-wave transceivers that leverage LAS, physical and media access control layer algorithms utilizing LAS, and low-cost packaging with emerging additive manufacturing technology. The project is led by the University of South Florida and Oregon State University, leveraging industrial collaboration partnerships with Keysight Technologies for mm-wave device, system, network characterization, and GlobalFoundries for silicon integrated circuit design and fabrication. The main contribution of this project is the LAS architecture: It outperforms traditional hybrid MIMO solutions by reducing hardware complexity and power consumption with minimal impact on wireless channel capacity per chain, resulting in significantly higher energy efficiency measured by data rate per unit power. The second major advance is to address system and hardware challenges in realizing scalable integrated mm-wave LAS transceivers to achieve this superior energy efficiency. The third major advance is addressing the cost effectiveness of mm-wave network deployment within the mass-scale communications market through innovative packaging and integration solutions using additive manufacturing.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.

对无线数据的日益增长的需求已经引起了对在毫米波频带处的无线通信的兴趣，在毫米波频带处有大量频谱可用，从而实现下一代无线网络的高数据速率。然而，传统的毫米波链路需要高功率发射机，这使得发射机效率至关重要。此外，这些链接必须同时支持多个用户。该项目将开发在毫米波频率下运行的节能和频谱高效的发射机和接收机，并在无线链路的各个层面进行创新，从通信协议到发射机/接收机集成电路和天线/透镜设计。此外，还将研究用于低成本制造毫米波阵列的先进3D打印技术。从技术角度来看，拟议的毫米波网络架构提供高带宽，低延迟和低成本的通信解决方案，将创造更多的高科技就业机会，并产生重大的经济影响。该项目的教育影响包括课程改进，研究生课程开发和研究生的研究培训，其中还包括对专业发展和研究管理的重视。该项目还将扩大高中生和代表性不足群体的学生的研究机会，创造和扩大STEM学生的管道。与行业合作伙伴的密切合作将促进技术进步的传播，同时沿着为参与该项目的学生提供重要的培训机会。具有数百个单元的大规模天线阵列，能够实现高增益和多输入多输出（MIMO）/多波束成形，对于毫米波频率的多用户无线链路具有吸引力。然而，由于每个天线元件需要昂贵且耗电的毫米波信号链、模数转换器和数模转换器，通过数字波束成形实现这种MIMO操作是禁止的。作为一种解决方案，具有减少数量的mm波信号链的混合MIMO架构最近已经吸引了对多个MIMO流传输的实际实现的兴趣。然而，这些架构在频谱和能量效率方面仍然表现出缺点，并且没有解决硬件复杂性问题。该项目旨在通过透镜天线子阵列（LAS）方法解决大型毫米波阵列在能源效率、频谱效率和硬件复杂性方面的根本挑战。该研究计划基于端到端的调查，包括LAS方案中的天线阵列设计，利用LAS的毫米波收发器，利用LAS的物理和媒体访问控制层算法，以及新兴增材制造技术的低成本封装。该项目由南佛罗里达大学和俄勒冈州州立大学牵头，利用与是德科技在毫米波器件、系统和网络特性方面的工业合作伙伴关系，以及与GlobalFoundries在硅集成电路设计和制造方面的合作伙伴关系。该项目的主要贡献是LAS架构：它通过降低硬件复杂性和功耗，对每条链的无线信道容量影响最小，从而大大提高了以每单位功率数据速率衡量的能源效率，从而优于传统的混合MIMO解决方案。第二个主要进步是解决实现可扩展集成毫米波LAS收发器的系统和硬件挑战，以实现这种上级能效。第三个重大进展是通过使用增材制造的创新封装和集成解决方案，解决大规模通信市场中毫米波网络部署的成本效益问题。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

IoTShare: A Blockchain-Enabled IoT Resource Sharing On-Demand Protocol for Smart City Situation-Awareness Applications

• DOI: 10.1109/jiot.2020.3004441
• 发表时间: 2020-10
• 期刊: IEEE Internet of Things Journal
• 影响因子: 10.6
• 作者: B. Hamdaoui;M. Alkalbani;A. Rayes;N. Zorba

A Broadband LNA and Sub-Harmonic Mixer Based Multi-Mode RX in 22nm CMOS

• DOI: 10.23919/eumic58042.2023.10288916
• 发表时间: 2023-09
• 期刊: 2023 18th European Microwave Integrated Circuits Conference (EuMIC)
• 作者: Soumya Gupta;Benjamin Jann;Mostafa Essawy;Kareem Rashed;Arun S. Natarajan

Deep Learning-Enabled Zero-Touch Device Identification: Mitigating the Impact of Channel Variability Through MIMO Diversity

支持深度学习的零接触设备识别：通过 MIMO 分集减轻信道变化的影响

• DOI: 10.1109/mcom.001.2200506
• 发表时间: 2023
• 期刊: IEEE Communications Magazine
• 影响因子: 11.2
• 作者: Hamdaoui, Bechir;Basha, Nora;Sivanesan, Kathiravetpillai
• 通讯作者: Sivanesan, Kathiravetpillai

Leveraging MIMO Transmit Diversity for Channel-Agnostic Device Identification

• DOI: 10.1109/icc45855.2022.9838976
• 发表时间: 2022-05
• 期刊: ICC 2022 - IEEE International Conference on Communications
• 作者: N. Basha;B. Hamdaoui;K. Sivanesan

Scalable spectrum database construction mechanisms for efficient wideband spectrum access management

• DOI: 10.1016/j.phycom.2021.101318
• 发表时间: 2021-03
• 期刊: Phys. Commun.
• 作者: Bassem Khalfi;B. Hamdaoui;M. Guizani;Abdurrahman Elmaghbub