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式增长为各种领域带来了新的应用和服务，包括健康、交通、能源、制造和娱乐。该项目的重点是开发能源和频谱效率，分布式多址接入策略的动态和大规模的无线网络下，严格的能源和延迟的要求，预计在新兴的应用。这项工作将有助于开发多种技术，改善整个社会的生活。例如，这项工作可以支持下一代通信技术，用于大规模物联网（IoT）应用和自动驾驶汽车应用。此外，教育是该项目的核心组成部分。在这个项目中开发的新理论和算法被整合到三所大学的研究生课程中。本科生和研究生参与了该项目，通过本科顶点和硕士毕业项目在俄亥俄州州立大学。该项目探讨了分布式频谱接入方法的基本能量和频谱效率的权衡，并开发了自适应和相关的策略，拥抱和控制随机性与效率保证动态用户与延迟敏感的流量。此外，该设计通过观察人类在简单的多路访问游戏中的反应，提供简单的人类行为模型和简单的人类感知质量指标，并通过设计可以适应意外事件或动作的方法，将人类纳入循环。该项目的综合分析和实现方法利用了系统的高维性，同时也克服了大规模实现和测试的困难。特别是，该项目开发了大规模频谱接入的平均场技术和分析。该项目开发的新的现实世界的实验策略，通过利用我们的随机化解决方案和上述平均场方法的集成简化，在小型试验台上模拟大规模系统操作。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影响审查标准进行评估，被认为值得支持。

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