EARS: SpecSense: Bringing Spectrum Sensing to the Masses

EARS：SpecSense：将频谱传感带给大众

• 批准号: 1642965
• 负责人: Samir Das
• 金额: $ 80万
• 依托单位: SUNY at Stony Brook
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-10-01 至 2021-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1642965&HistoricalAwards=false
• 关键词: EARS; SpecSense; Bringing; Spectrum; Sensing

With the explosion of mobile data, there is a growing realization that the radio frequency spectrum must be treated as an important resource that is in limited supply. Policy makers and researchers alike are promoting various forms of spectrum sharing models to improve spectrum utilization. Just like any other resource with mismatched demand and supply, all steps towards better utilization of radio spectrum have also increased the need for large scale spectrum monitoring. This serves two key purposes: (i) it helps identify available spectrum opportunities, making spectrum sharing systems more effective, (ii) it can help us develop deeper understanding of spectrum usage and demand over time and space. Large-scale spectrum monitoring can feed into multitudes of 'spectrum-aware' applications forming an entire ecosystem of spectrum data, analytics and apps. The proposed project develops an end-to-end enabling platform called SpecSense to support this vision. SpecSense (i) crowdsources spectrum monitoring using low-cost, low-power custom-designed hardware, and (ii) provides necessary library and interface support for spectrum-aware apps via a central spectrum server/database platform. This project is expected to foster interest in spectrum data marketplaces facilitated by crowdsourced spectrum sensing. This can engender commercial interests in various aspects of the spectrum data ecosystem. In many fields, e.g., healthcare, education, Internet-of-Things, there is a tremendous need for mobile bandwidth and innovation is stunted due to a lack of bandwidth. Success in this project will drive such innovations. The project will also contribute to various educational activities for students with a range of academic preparations.This project addresses several of the core intellectual challenges in developing SpecSense, viz., (1) Exploration of FPGA-based sensors where sensing algorithms are built into the FPGA, with accompanying tools to automatically implement and optimize these algorithms so that they provide the desired trade-off between power and performance; (2) Novel interpolation techniques to estimate spectrum occupancy in both spatial and temporal domains; (3) Algorithms to support optimized selection of sensors to minimize overall sensing cost; (4) Development of an end-to-end testbed and evaluation over a range of spectrum-aware applications. The project team has a range of expertise in topics relevant to the proposal, such as automated hardware design, digital signal processing, detection and estimation, wireless networking, networking algorithms, and networked systems design.

随着移动数据的爆炸式增长，人们越来越认识到必须将射频频谱视为供应有限的重要资源。政策制定者和研究人员都在推广各种形式的频谱共享模型，以提高频谱利用率。就像需求和供应不匹配的任何其他资源一样，所有旨在更好地利用无线电频谱的步骤也增加了对大规模频谱监测的需求。这有两个关键目的：（i）它有助于识别可用的频谱机会，使频谱共享系统更加有效，（ii）它可以帮助我们更深入地了解频谱使用情况以及随时间和空间的需求。 大规模频谱监控可以馈送到众多“频谱感知”应用程序中，形成频谱数据、分析和应用程序的整个生态系统。拟议的项目开发了一个名为 SpecSense 的端到端支持平台来支持这一愿景。 SpecSense (i) 使用低成本、低功耗定制设计硬件进行众包频谱监测，(ii) 通过中央频谱服务器/数据库平台为频谱感知应用程序提供必要的库和接口支持。该项目预计将激发人们对众包频谱传感促进的频谱数据市场的兴趣。这可以在频谱数据生态系统的各个方面产生商业利益。在医疗、教育、物联网等许多领域，对移动带宽的需求巨大，而创新却因带宽不足而受到阻碍。该项目的成功将推动此类创新。该项目还将为学生的各种教育活动做出贡献，为学生提供一系列学术准备。该项目解决了开发 SpecSense 时的几个核心智力挑战，即 (1) 探索基于 FPGA 的传感器，其中传感算法内置于传感器中。 FPGA，以及自动实施和优化这些算法的配套工具，以便它们在功耗和性能之间提供所需的权衡； (2) 估计空间和时间域频谱占用率的新颖插值技术； (3) 支持传感器优化选择的算法，以最大限度地降低总体传感成本； (4) 开发端到端测试平台并评估一系列频谱感知应用程序。项目团队在与提案相关的主题上拥有一系列专业知识，例如自动化硬件设计、数字信号处理、检测和估计、无线网络、网络算法和网络系统设计。

项目成果

SpecSense: Crowdsensing for efficient querying of spectrum occupancy

SpecSense：群体感知，用于高效查询频谱占用情况

• DOI: 10.1109/infocom.2017.8057113
• 发表时间: 2017-05-01
• 期刊: IEEE INFOCOM 2017 - IEEE Conference on Computer Communications
• 作者: Ayon Chakraborty;Md. Shaifur Rahman;Himanshu Gupta;Samir R Das
• 通讯作者: Samir R Das

Selection of Sensors for Efficient Transmitter Localization

选择用于高效发射机定位的传感器

• DOI: 10.1109/infocom41043.2020.9155230
• 发表时间: 2020-01
• 期刊: IEEE INFOCOM 2020 - IEEE Conference on Computer Communications
• 作者: Bhattacharya, Arani;Zhan, Caitao;Gupta, Himanshu;Das, Samir R.;Djuric, Petar M.
• 通讯作者: Djuric, Petar M.

Spectrum Patrolling With Crowdsourced Spectrum Sensors

使用众包频谱传感器进行频谱巡逻

• DOI: 10.1109/tccn.2019.2939793
• 发表时间: 2020-03-01
• 期刊: IEEE Transactions on Cognitive Communications and Networking
• 影响因子: 8.6
• 作者: A. Bhattacharya;Ayon Chakraborty;Samir R Das;Himanshu Gupta;P. Djurić
• 通讯作者: P. Djurić

Multiple Transmitter Localization under Time-Skewed Observations

时间偏差观测下的多发射机定位

• DOI: 10.1109/dyspan.2019.8935739
• 发表时间: 2019-11-01
• 期刊: 2019 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN)
• 作者: Mohammad Ghaderibaneh;Mallesham Dasari;Himanshu Gupta
• 通讯作者: Himanshu Gupta

Creating Spatio-temporal Spectrum Maps from Sparse Crowdsensed Data

从稀疏的群体感知数据创建时空谱图

• DOI: 10.1109/wcnc.2019.8885811
• 发表时间: 2019-04-01
• 期刊: 2019 IEEE Wireless Communications and Networking Conference (WCNC)
• 作者: Md. Shaifur Rahman;Himanshu Gupta;Ayon Chakraborty;S. Das
• 通讯作者: S. Das