CRII: CNS: IoT-aware Dynamic Spectrum Sharing in 5G C-RAN with Reinforcement Learning

物联网（IoT）预计将彻底改变交通、健康、电网、智慧城市运营等日常生活的许多关键领域。为了便于部署，3GPP提出了窄带物联网（NB-IoT）和LTE cat-M等新技术，其特点是i)增强了覆盖范围；Ii)低频谱要求，iii)简单廉价的无线电芯片组；四)降低能源消耗；V)增加冗余。采用它们的一个关键优势是NB-IoT和LTE- m可以部署在相同的频谱上，并且可以使用与蜂窝技术（例如LTE和5G）相同的基站。然而，物联网网络的大规模部署可能会对蜂窝基础设施产生潜在的破坏性影响，从而降低人类用户的服务质量。该项目的目标是进一步研究新技术，以提高传统的以人为中心的技术和物联网设备之间共享可用无线电资源的能力。该项目涉及本科生和研究生的研究，并在布法罗日期间组织了5G和物联网的特别会议，该活动汇集了来自学术界，工业界和市政当局的关键利益相关者，讨论该领域的最新进展。该项目的目标将通过i)提供一个全面的物联网感知框架来表征移动网络中的流量，ii)为基于强化学习的5G C-RAN制定一个动态频谱共享模型来实现。这两个重点将按照3GPP在物联网流量用例和C-RAN规范方面的指导方针进行开发。此外，将使用物联网设备和网络基站位置的真实数据来增加所提出方法的现实适应性。本研究将结合随机分布（如泊松点过程），从公开来源检索的真实数据。将部署一个基于网络的框架，用于收集和共享物联网和人类交通的时空数据。该框架将公开提供，并将为无线网络和物联网的研究人员提供（i)分析模型的现实场景，（ii）网络模拟/仿真研究的输入数据，以及iii）机器学习算法的训练数据。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。

The Internet of Things (IoT) is expected to revolutionize many key sectors of everyday life, such as transportation, health, power grid, and smart cities operation. To facilitate its deployment, 3GPP has proposed novel technologies, such as NarrowBand-IoT (NB-IoT) and LTE cat-M, characterized by i) enhanced coverage; ii) low spectrum requirements, iii) simple and cheap radio chipsets; iv) reduced energy consumption; v) increased redundancy. A key advantage to their adoption is the fact that NB-IoT and LTE-M can be deployed on the same spectrum and can use the same base stations as cellular technologies (e.g., LTE and 5G). However, massive deployment of IoT networks can have a potentially disruptive effect on cellular infrastructures, thus degrading the quality of service by human users. The objective of this project is to further research on novel techniques to improve the ability to share the available radio resources between traditionally human-centric technologies and IoT devices. The project involves undergraduate and graduate students in research, and organizes a special session within the Buffalo Day for 5G and IoT, an event that brings together critical stakeholders from academia, industry and municipalities to discuss the latest advancements in this domain.The objective of this project will be attained by i) providing a comprehensive IoT-aware framework to characterize traffic in mobile networks, ii) formulating a dynamic spectrum sharing model for the 5G C-RAN based on Reinforcement Learning. The two thrusts will be developed following the guidelines by 3GPP in terms of IoT traffic use cases and C-RAN specifications. Moreover, real data on the position of IoT devices and of network base stations will be employed to increase the fitness to reality of the proposed methodology. Real data, retrieved from publicly available sources, will be used in this research in combination with random distributions, such as Poisson Point Processes. A web-based framework for the collection and sharing of spatial and temporal data on IoT and human traffic will be deployed. The framework will be made publicly available and will provide researchers in wireless networks and IoT with (i) realistic scenarios for their analytical models, (ii) input data for their network simulations/emulations research, and iii) training data for their machine-learning algorithms.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.