Collaborative Research: SWIFT: SMALL: Autonomously Reconfigurable Hardware-Reduced Wideband Transceivers for Efficient Passive-Active Spectrum Coexistence
Collaborative Research: SWIFT: SMALL: Autonomously Reconfigurable Hardware-Reduced Wideband Transceivers for Efficient Passive-Active Spectrum Coexistence
批准号:
2030234
负责人:
Dimitris Pados
金额:
$24.99万
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
项目状态:
已结题
起止时间:
2020-09-01 至 2024-08-31
中文摘要
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英文摘要
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.
期刊论文(5)
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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)
影响因子:
--
作者:
[Stepan Mazokha;Sanaz Naderi;Georgios I. Orfanidis;G. Sklivanitis;D. Pados;J. Hallstrom]
通讯作者:
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.
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]
通讯作者:
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
DOI:
--
发表时间:
2023
期刊:
SPIE Defense + Commercial Sensing
影响因子:
--
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
影响因子:
--
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.
MRI: Development of a mmWave-Networked Robotic Testbed for Multi-Agent AI Learning and Operations
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批准号:2117822
-
项目类别:Standard Grant
-
资助金额:$100.0万
-
财政年份:2021
-
负责人:Dimitris Pados
-
依托单位:
Making the Master's Degree in Artificial Intelligence Accessible to High-Achieving Low-Income Students
-
批准号:2030854
-
项目类别:Standard Grant
-
资助金额:$100.0万
-
财政年份:2020
-
负责人:Dimitris Pados
-
依托单位:
I-Corps Sites: Type I - Florida Atlantic University I-Corps Site for Advancing Entrepreneurship and Innovation
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批准号:1829243
-
项目类别:Continuing Grant
-
资助金额:$25.5万
-
财政年份:2018
-
负责人:Dimitris Pados
-
依托单位:
NeTS: Small: Towards Ubiquitous Multimedia Sensing through Compressive Video Streaming
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批准号:1117121
-
项目类别:Standard Grant
-
资助金额:$45.0万
-
财政年份:2011
-
负责人:Dimitris Pados
-
依托单位:
Smart Antennas and DS/CDMA Communications: Basic Algorithmic Developments and Hardware Prototyping
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批准号:0073660
-
项目类别:Standard Grant
-
资助金额:$14.03万
-
财政年份:2000
-
负责人:Dimitris Pados
-
依托单位:
Joint Space-Time Auxiliary-Vector Filtering for DS/CDMA Systems with Antenna Arrays
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批准号:9805359
-
项目类别:Standard Grant
-
资助金额:$6.0万
-
财政年份:1998
-
负责人:Dimitris Pados
-
依托单位:
国内基金
海外基金
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