CCF: Small: Online Learning and Exploitation of the Radio Frequency Spectrum with Sub-Nyquist Sampling
CCF:小型:采用亚奈奎斯特采样的射频频谱在线学习和利用
基本信息
- 批准号:1534957
- 负责人:
- 金额:$ 40.5万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2014
- 资助国家:美国
- 起止时间:2014-12-01 至 2018-07-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
This project will close technical gaps to enable cognitive radio receivers to explore the radio frequency spectrum online, using the most advanced form of Analog to Digital conversion, referred to as Finite Rate of Innovation (FRI) sampling coupled with the most advanced learning techniques. We plan to use the well-established framework of the multi-armed bandit (MAB) problem, which models the situation of a cognitive radio agent that simultaneously attempts to acquire new knowledge and to optimize its decisions based on what it has previously learned. Our main contribution lies in combining this framework with this novel Analog to Digital receiver front-end, sampling rate below the so called Nyquist limit, adaptively tuning parameters in the sampling structure to sense spectrum opportunities over a much wider range of frequencies than was previously considered possible, and specifically further below what is attainable myopically, without adaptation. The outcome of our study is a cohesive system model for a cognitive sensors, endowed with a decision engine that can optimize not only what to sample but also how to sample analog signals, leveraging on its expected success in finding spectrum holes. The project will explore the complexity of the overall architecture and, ultimately, evaluate the potential benefits of a cognitive MAB-FRI receiver. By moving learning algorithms a step closer to manage directly the data-acquisition interface to the physical world, the research as broad implications in a variety of related sensing problems. The project will also include activities to engage students in classrooms presenting the basic mathematical tools used in this research and minorities in research projects that contribute to advance the broad field of adaptive systems.
该项目将缩小技术差距,使认知无线电接收器能够在线探索无线电频谱,使用最先进的模数转换形式,即有限创新率(FRI)采样,再加上最先进的学习技术。我们计划使用多臂盗匪(MAB)问题的完善框架,该框架模拟了认知无线电代理的情况,该代理同时尝试获取新知识并基于先前所学的知识优化其决策。我们的主要贡献在于将该框架与这种新颖的模拟到数字接收器前端相结合,采样率低于所谓的奈奎斯特极限,自适应地调整采样结构中的参数,以在比以前认为可能的频率范围更宽的频率范围内感知频谱机会,特别是低于可实现的近视范围,无需自适应。我们的研究结果是一个认知传感器的内聚系统模型,它被赋予了一个决策引擎,不仅可以优化采样的内容,还可以优化如何采样模拟信号,利用它在寻找频谱洞方面的预期成功。该项目将探索整体架构的复杂性,并最终评估认知MAB-FRI接收器的潜在优势。通过使学习算法更接近于直接管理与物理世界的数据采集接口,该研究在各种相关的传感问题中具有广泛的意义。该项目还将包括让学生在课堂上展示本研究中使用的基本数学工具的活动,以及有助于推进适应性系统广泛领域的研究项目中的少数群体。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Anna Scaglione其他文献
Stochastic Dynamic Network Utility Maximization with Application to Disaster Response
随机动态网络效用最大化及其在灾难响应中的应用
- DOI:
- 发表时间:
2024 - 期刊:
- 影响因子:0
- 作者:
Anna Scaglione;Nurullah Karakoç - 通讯作者:
Nurullah Karakoç
2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020, Austin, TX, USA, March 23-27, 2020
2020 IEEE 国际普适计算和通信研讨会研讨会,PerCom Workshops 2020,美国德克萨斯州奥斯汀,2020 年 3 月 23-27 日
- DOI:
- 发表时间:
2020 - 期刊:
- 影响因子:0
- 作者:
Yuan Lai;Gonzalo J. Martinez;Stephen M. Mattingly;Shayan Mirjafari;Subigya Nepal;Andrew T Campbell;A. Dey;Aaron D. Striegel;Marco Jansen;Fatjon Seraj;Wei Wang;P. Havinga;Kaijie Zhang;Zhiwen Yu;Dong Zhang;Zhu Wang;Bin Guo;Julian Graf;Katrin Neubauer;Sebastian Fischer;Rudolf Hackenberg;Elliott Wen;Gerald Weber;Javier Rojo;Daniel Flores;J. García;J. M. Murillo;Javier Berrocal;Mingyu Hou;Tianyu Kang;Li Guo;Edison Thomaz;Beichen Yang;Min Sun;Xiaoyan Hong;Xiaoming Guo;P. Barsocchi;A. Crivello;Michele Girolami;Fabio Mavilia;Vivek Chandel;Shivam Singhal;Avik Ghose;Tetsushi Matsuda;Toru Inada;Susumu Ishihara;Luay Alawneh;Belal Mohsen;Mohammad Al;Ahmed S. Shatnawi;Mahmoud Al;N. B. Rabah;Eoin Brophy;W. Muehlhausen;A. Smeaton;Tomás E. Ward;S. Maskey;S. Badsha;Shamik Sengupta;Ibrahim Khalil;Stanisław Saganowski;Anna Dutkowiak;A. Dziadek;Maciej Dziezyc;Joanna Komoszynska;Weronika Michalska;Adam G. Polak;Michal Ujma;Przemysław Kazienko;Nurullah Karakoç;Anna Scaglione;Fatemeh Mirzaei;Jonathan Lam;Roberto Manduchi;R. K. Ramakrishnan;R. Gavas;Lalit Venkata Subramaninan Viraraghavan;Kumar Hissaria;Arpan Pal;P. Balamuralidhar;S. Ditton;Ali Tekeoglu;K. Bekiroglu;Seshadhri Srinivasan;E. Tonkin;Miquel Perello Nieto;Haixia Bi;Antonis Vafeas;Yuri Tani;M. Garcia;A. Konios;M. A. Mustafa;C. Nugent;G. Morrison;Noah Sieck;Cameron Calpin;Mohammad S. Almalag;M. M. Sandhu;Kai Geissdoerfer;Sara Khalifa;Raja Jurdak;Marius Portmann;Brano Kusy;Alwyn Burger;Chao Qian;Gregor Schiele;Domenik Helms;Peter Zdankin;Marian Waltereit;V. Matkovic;Torben Weis;Syafiq Al Atiiq;Christian Gehrmann;Jae Woong Lee;Sumi Helal;Mathias Mormul;Christoph Stach;L. Krupp;G. Bahle;Agnes Gruenerbl;P. Lukowicz;Nicholas Handaja;Brent Lagesse;Clémentine Gritti;Dennis Przytarski;Bernhard Mitschang;Yeongjun Jeon;Kukho Heo;Soon Ju Kang;Sandeep Biplav Srivastava;Singh Sandha;Vaskar Raychoudhury;Sukanya Randhawa;V. Kapoor;Anmol Agrawal;Young D. Kwon;Kirill A. Shatilov;Lik;Serkan Kumyol;Kit;Yui;Pan Hui;Brittany Lewis;Joshua Hebert;Krishna Venkatasubramanian;Matthew Provost;Kelly Charlebois;Kristina Yordanova;Albert Hein;T. Kirste;Lien;Jun;Wei;Casper Van Gheluwe;I. Šemanjski;Suzanne Hendrikse;S. Gautama;Furqan Jameel;Zheng Chang;Riku Jäntti;Sergio Laso;M. Linaje;Ikram Ullah;N. Meratnia;Steven M. Hernandez;Eyuphan Bulut;Amiah Gooding;Matthew Martin;Maxwell Minard;Smruthi Sandhanam;Travis Stanger;Yana Alexandrova;Ashfaq Khokhar;Goce Trajcevski;Utsav Goswami;Kevin Wang;Gabriel Nguyen;Federico Montori;L. Bedogni;Gianluca Iselli;L. Bononi;Saptaparni Kumar;Haochen Pan;Roger Wang;Lewis Tseng;K. Hirayama;S. Saiki;Masahide Nakamura;Kiyoshi Yasuda;Samy El;Ismail Arai;Ahmad Salman;B. B. Park;Yuya Sano;Yuito Sugata;Teruhiro Mizumoto;H. Suwa;K. Yasumoto;P. Kouris;Marietta Sionti;Chrysovalantis Korfitis;Stella Markantonatou;Naima Khan;Nirmalya Roy;D. Jaiswal;D. Chatterjee;Ramesh Kumar;Ana Cristina Franco;Da Silva;Pascal Hirmer;Jan Schneider;Seda Ulusal;Matheus Tavares;Tomokazu Matsui;Kosei Onishi;Shinya Misaki;Manato Fujimoto;Hayata Satake;Yuki Kobayashi;Ryotaro Tani;Hiroshi Shigeno;Avijoy Chakma;Abu Zaher;Md Faridee;M Sajjad Hossain;Cleo Forman;Pablo Thiel;Raymond Ptucha;Miguel Dominguez;Cecilia Ovesdotter Alm;S. Mozgai;Arno Hartholt;Albert Rizzo - 通讯作者:
Albert Rizzo
Network-Constrained Reinforcement Learning for Optimal EV Charging Control
用于最佳电动汽车充电控制的网络约束强化学习
- DOI:
- 发表时间:
2023 - 期刊:
- 影响因子:0
- 作者:
Tong Wu;Anna Scaglione;Adrian;Daniel Arnold;S. Peisert - 通讯作者:
S. Peisert
Routing and data compression in sensor networks: stochastic models for sensor data that guarantee scalability
- DOI:
10.1109/isit.2003.1228188 - 发表时间:
2003-09 - 期刊:
- 影响因子:0
- 作者:
Anna Scaglione - 通讯作者:
Anna Scaglione
Statistical analysis of the capacity of MIMO frequency selective Rayleigh fading channels with arbitrary number of inputs and outputs
- DOI:
10.1109/isit.2002.1023550 - 发表时间:
2002-06 - 期刊:
- 影响因子:0
- 作者:
Anna Scaglione - 通讯作者:
Anna Scaglione
Anna Scaglione的其他文献
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{{ truncateString('Anna Scaglione', 18)}}的其他基金
I-Corps: Geospatial Trend Detection for Hydro-power and Critical Infrastructure Design
I-Corps:水电和关键基础设施设计的地理空间趋势检测
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$ 40.5万 - 项目类别:
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Advancing Graph Signal Processing Techniques for Monitoring and Control of Electric Distribution Power Systems
先进的图形信号处理技术用于配电电力系统的监测和控制
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Standard Grant
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CCF-BSF:CIF:小:多代理优化算法中恶意行为的识别和隔离
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1714672 - 财政年份:2017
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$ 40.5万 - 项目类别:
Standard Grant
EAGER: The Identification of Social Systems Trust: Theory and Experimental Validation
EAGER:社会系统信任的识别:理论与实验验证
- 批准号:
1553746 - 财政年份:2015
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$ 40.5万 - 项目类别:
Standard Grant
Collaborative Research: EAGER: Renewables: A function space theory for continuous-time flexibility scheduling in electricity markets
合作研究:EAGER:可再生能源:电力市场连续时间灵活性调度的函数空间理论
- 批准号:
1549923 - 财政年份:2015
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$ 40.5万 - 项目类别:
Standard Grant
CIF: Large: Collaborative Research: Cooperation and Learning Over Cognitive Networks
CIF:大型:协作研究:认知网络上的合作与学习
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Continuing Grant
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- 批准号:
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$ 40.5万 - 项目类别:
Standard Grant
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CIF:大型:协作研究:认知网络上的合作与学习
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$ 40.5万 - 项目类别:
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NetS:媒介:协作研究:通过稳健的协作跨层设计释放无线接入网络的容量
- 批准号:
0905267 - 财政年份:2009
- 资助金额:
$ 40.5万 - 项目类别:
Standard Grant
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