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

低成本无线设备的爆炸式增长为医疗、交通、能源、制造和娱乐等不同领域带来了新的应用和服务。该项目侧重于开发能源和频谱效率高的分布式多址策略，用于动态和大规模无线网络，以满足新兴应用对能源和延迟的严格要求。这项工作将促进多种技术的发展，从而改善整个社会的生活。例如，这项工作可以支持大规模物联网（IoT）应用和自动驾驶汽车应用的下一代通信技术。此外，教育是这个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。本科生和研究生通过俄亥俄州立大学的本科毕业项目和硕士毕业项目参与该项目。本项目探索分布式频谱接入方法的基本能量和频谱效率权衡，并为具有延迟敏感业务的动态用户开发具有效率保证的自适应和相关策略，以拥抱和控制随机性。此外，该设计通过观察人类在简单的多通道游戏中的反应，提供简单的人类行为模型和简单的人类感知质量指标，并通过设计能够适应意外事件或行动的方法，将人类融入到循环中。本项目的分析与实现相结合的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。在这个项目中开发的新的现实世界实验策略通过利用我们的随机解决方案和上述平均场方法的集成所带来的简化，在一个小型测试平台上模拟大规模系统操作。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.