Collaborative Research: SWIFT: SMALL: Autonomously Reconfigurable Hardware-Reduced Wideband Transceivers for Efficient Passive-Active Spectrum Coexistence

合作研究：SWIFT：SMALL：自主可重构硬件精简宽带收发器，实现高效无源-有源频谱共存

• 批准号: 2030234
• 负责人: Dimitris Pados
• 金额: $ 24.99万
• 依托单位: Florida Atlantic University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2030234&HistoricalAwards=false
• 关键词: Collaborative; Research; SWIFT; SMALL; Autonomously

Commercial and military wireless systems, radio astronomy observatories, weather radar systems, and other applications, frequently referred to as “passive users,” need to operate in quiet electromagnetic environments with limited interference. Passive users commonly strive to observe faint signals emitted by distant non-coordinating transmitters and their frequency or time separation from other sources may not be possible as they often continuously utilize large swaths of radio spectrum to detect and monitor physical processes that have electromagnetic presence at different frequencies. To fully reap the benefits of spectrum coexistence in 5G and beyond, there is a need to employ passive-user protection approaches that strike an optimal balance between two key objectives, namely reliably protect passive users from interference and maximize spectrum access opportunities for active users. This project addresses foundational challenges in spectrum coexistence by developing a novel low-cost hardware-reduced and multi-parameter reconfigurable ultra-wideband transceiver that optimizes passive-active spectrum sharing across a broad frequency range. The portability and adaptability of this new transceiver makes it attractive for next-generation mobile wireless platforms, including a variety of autonomous ground and/or airborne platforms, cellular base-stations, unmanned airborne and satellite communication systems and specifically impact 5G, Wi-Fi and future applications of connected autonomy. Through this project the PIs propose to train undergraduate, graduate, and postdoctoral students targeting Hispanic, women, and other underrepresented groups in science, technology, engineering and mathematics (STEM) through specialized outreach efforts and curriculum development.The project brings together an interdisciplinary team of researchers with complementary expertise ranging from Radio Frequency (RF) front-end hardware design, to Physical (PHY) and Medium-Access Control (MAC) layer optimization, to artificial intelligence (AI) theory and practice. To avoid hardware constraints and lack of reconfigurability imposed by analog and hybrid beamforming architectures the project develops a novel digital ultra-wideband beamforming architecture that enables: 1) Efficient spectrum utilization and interference-free passive-active coexistence through robust space-time-frequency sensing and cross-layer optimization at the PHY and MAC layers; 2) autonomous hardware multi-parameter tunability for ultra-wideband operation across 5G bands; and 3) practical realization of low-cost, versatile hardware-reduced wireless systems through new artificial-neural-network-aided code multiplexed array front-ends. Robust spectrum sensing involves new L1-norm principal-component analysis to assess the quality (validity and completeness) of the collected/sensed spectrum data and produce high-confidence power propagation and spatial coordination maps of active spectrum users. The project leverages high-quality radio maps, autonomous sub-arraying, multi-band, and multi-parameter reconfigurability across large bandwidths and incorporates reinforcement learning strategies to address the cross-layer PHY/MAC and front-end control problem for harmonious passive-active spectrum coexistence. The complete outcome of this project is expected to be an autonomously reconfigurable hardware-reduced platform that will allow integration of ultra-wideband sensing and beamforming functionalities in small-form-factor software-defined radio platforms. This new class of low-cost, versatile, hardware-reduced wireless transceivers that integrate multiple radio chains into a practical single lightweight package is enabled by the application of neural networks to cancel both linear and non-linear components of inter-channel interference that arise during the multiplexing of multiple non-orthogonal spread signal paths.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.

商用和军用无线系统、射电天文观测台、气象雷达系统和其他应用（通常称为“被动用户”）需要在干扰有限的安静电磁环境中运行。无源用户通常努力观察由远距离非协调发射机发射的微弱信号，并且它们与其他源的频率或时间分离可能是不可能的，因为它们经常连续地利用大范围的无线电频谱来检测和监测具有不同频率的电磁存在的物理过程。为了充分获得5G及以后频谱共存的好处，需要采用被动用户保护方法，在两个关键目标之间取得最佳平衡，即可靠地保护被动用户免受干扰，并最大限度地增加主动用户的频谱接入机会。该项目通过开发一种新颖的低成本硬件减少和多参数可重新配置的超宽带收发器来解决频谱共存的基本挑战，该收发器在宽频率范围内优化无源-有源频谱共享。这种新型收发器的便携性和适应性使其对下一代移动的无线平台具有吸引力，包括各种自主地面和/或机载平台，蜂窝基站，无人机载和卫星通信系统，特别是影响5G，Wi-Fi和未来的连接自主应用。通过这个项目，PI建议通过专业的外展工作和课程开发，培养本科生，研究生和博士后学生，目标是西班牙裔，妇女和其他代表性不足的群体在科学，技术，工程和数学（STEM）。该项目汇集了一个跨学科的研究人员团队，具有互补的专业知识，从射频（RF）前端硬件设计，物理（PHY）和媒体访问控制（MAC）层优化，人工智能（AI）理论和实践。为了避免模拟和混合波束成形架构所带来的硬件限制和缺乏可重构性，该项目开发了一种新型的数字超宽带波束成形架构，该架构能够：1）通过在PHY和MAC层的鲁棒的空时频感测和跨层优化来实现高效的频谱利用和无干扰的无源-有源共存; 2）用于跨5G频带的超宽带操作的自主硬件多参数可调性;以及3）通过新的人工神经网络辅助码复用阵列前端实际实现低成本、多功能硬件减少的无线系统。鲁棒频谱感知涉及新的L1范数主成分分析，以评估收集/感知的频谱数据的质量（有效性和完整性），并产生高置信度的功率传播和空间协调地图的活跃频谱用户。该项目利用高质量的无线电地图、自主子阵列、多频段和跨大带宽的多参数可重构性，并采用强化学习策略来解决跨层PHY/MAC和前端控制问题，以实现和谐的无源-有源频谱共存。该项目的完整成果预计将是一个自主可重新配置的硬件简化平台，该平台将允许在小型软件定义无线电平台中集成超宽带传感和波束成形功能。这种新型的低成本，多功能，通过应用神经网络来消除信道间干扰的线性和非线性分量，使得能够实现将多个无线电链路集成到实际的单个轻量封装中的硬件减少的无线收发器，该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的评估，被认为是值得支持的。影响审查标准。

项目成果

Single-Sample Direction-of-Arrival Estimation for Fast and Robust 3D Localization With Real Measurements from a Massive MIMO System

• DOI: 10.1109/icassp49357.2023.10096647
• 发表时间: 2023-06
• 期刊: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
• 作者: Stepan Mazokha;Sanaz Naderi;Georgios I. Orfanidis;G. Sklivanitis;D. Pados;J. Hallstrom

FFT calculation of the L1-norm principal component of a data matrix

数据矩阵的 L1 范数主成分的 FFT 计算

• DOI: 10.1016/j.sigpro.2021.108286
• 发表时间: 2021
• 期刊: Signal Processing
• 影响因子: 4.4
• 作者: Colonnese, Stefania;Markopoulos, Panos P.;Scarano, Gaetano;Pados, Dimitris A.
• 通讯作者: Pados, Dimitris A.

Low-Complexity Decoder for Overloaded Uniquely Decodable Synchronous CDMA

• DOI: 10.1109/access.2022.3170491
• 发表时间: 2018-06
• 期刊: IEEE Access
• 影响因子: 3.9
• 作者: Michel Kulhandjian;Hovannes Kulhandjian;C. D’amours;H. Yanikomeroglu;D. Pados;G. Khachatrian

Unsupervised training dataset curation for deep-neural-net RF signal classification

用于深度神经网络射频信号分类的无监督训练数据集管理

• 发表时间: 2023
• 期刊: SPIE Defense + Commercial Sensing
• 作者: Sklivanitis, George;Viloria, Jose A.;Tountas, Konstantinos;Pados, Dimitris A.;Bentley, Elizabeth Serena;Medley, Michael J.
• 通讯作者: Medley, Michael J.

Single-Sample Direction-of-Arrival Estimation by Hankel-matrix Decompositions

通过 Hankel 矩阵分解进行单样本到达方向估计

• DOI: 10.1109/ieeeconf56349.2022.10051870
• 发表时间: 2022
• 期刊: and Computers
• 作者: Orfanidis, Georgios I.;Pados, Dimitris A.;Sklivanitis, George;Bentley, Elizabeth S.;Suprenant, Joseph;Medley, Michael J.
• 通讯作者: Medley, Michael J.