SpecEES: Collaborative Research: Leveraging Randomization and Human Behavior for Efficient Large-Scale Distributed Spectrum Access

SpecEES：协作研究：利用随机化和人类行为实现高效的大规模分布式频谱访问

• 批准号: 1824393
• 负责人: Lei Ying
• 金额: $ 17.5万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-10-01 至 2020-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1824393&HistoricalAwards=false
• 关键词: SpecEES; Collaborative; Research; Leveraging; Randomization

An explosion of low-cost wireless devices promises new applications and services in diverse domains, including health, transportation, energy, manufacturing, and entertainment. This project focuses on developing energy and spectrum-efficient, distributed multi-access strategies for dynamic and large-scale wireless networks under the stringent energy and delay requirements that are expected in emerging applications. This work will enable the development of a multitude of technologies that can improve the life of society-at-large. For example, this work can support the next generation of communication technologies for large-scale Internet of Things (IoT) applications and autonomous vehicle applications. Moreover, education is a core component of this project. New theories and algorithms developed in this project are integrated into the graduate-level courses at the three universities. Undergraduate and graduate students are involved in the project through the undergrad capstone and masters graduation projects at the Ohio State University.This project explores the fundamental energy and spectrum-efficiency tradeoff of distributed spectrum access methods, and develops adaptive and correlated strategies that embrace and control randomness with efficiency guarantees for dynamic users with delay-sensitive traffic. In addition, the design incorporates humans into the loop by observing how humans react in simple multi-access games, providing simple human behavior models and simple human-perceived quality metrics, and by designing methods that can adapt to unexpected events or actions. A combined analysis and implementation approach of this project exploits high-dimensionality in the system while also overcoming difficulties for large-scale implementation and testing. In particular, the project develops mean-field techniques and analyses for large-scale spectrum access. Novel real-world experimentation strategies developed in this project emulate large-scale system operation in a small testbed by utilizing the simplification due to our randomized solutions and the integration of the aforementioned mean-field methods.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.

低成本无线设备的爆炸爆炸有望在包括健康，运输，能源，制造和娱乐活动在内的各个领域的新应用和服务。该项目着重于在新兴应用程序中预期的严格的能量和延迟要求下开发动态和大规模无线网络的能源和分布式的多访问策略。这项工作将使能够开发多种可以改善社会生活的技术。例如，这项工作可以支持大规模物联网（IoT）应用程序和自动驾驶汽车应用程序的下一代通信技术。此外，教育是该项目的核心组成部分。该项目中开发的新理论和算法都集成到三所大学的研究生级课程中。本科生和研究生通过俄亥俄州立大学的本科生和硕士学位毕业项目参与了该项目。本项目探讨了分布式光谱访问方法的基本能量和频谱效率的权衡，并开发自适应和相关的策略，这些策略可以通过效率和控制效率来保证具有延迟敏感性的动态效率。此外，该设计通过观察人类在简单的多访问游戏中的反应，提供简单的人类行为模型和简单的人类感知质量指标，以及设计可以适应意外事件或动作的方法，将人类纳入循环中。该项目的组合分析和实施方法利用了系统中的高维度，同时也克服了大规模实施和测试的困难。特别是，该项目开发了大规模频谱访问的平均场技术和分析。该项目中制定的新型现实世界实验策略在小型测试床上采用了大规模系统操作，该策略是由于我们的随机解决方案以及上述均值场方法的整合而简化的。这项奖项反映了NSF的法定任务，并通过使用基金会的知识优点和广泛影响来评估NSF的法定任务。

项目成果

A Mean Field Game Analysis of Distributed MAC in Ultra-Dense Multichannel Wireless Networks

• DOI: 10.1145/3323679.3326498
• 发表时间: 2019-07
• 期刊: IEEE/ACM Transactions on Networking
• 作者: D. Narasimha;S. Shakkottai;Lei Ying