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

CRII：CNS：通过强化学习在 5G C-RAN 中实现物联网感知动态频谱共享

• 批准号: 2105230
• 负责人: Filippo Malandra
• 金额: $ 17.5万
• 依托单位: SUNY at Buffalo
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2105230&HistoricalAwards=false
• 关键词: CRII; CNS; IoT; aware; Dynamic

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.

物联网（IoT）预计将彻底改变日常生活中的许多关键领域，如交通、健康、电网和智慧城市运营。为了促进其部署，3GPP已经提出了诸如窄带IoT（NB-IoT）和LTE cat-M的新颖技术，其特征在于i）增强的覆盖; ii）低频谱要求; iii）简单且廉价的无线电芯片组; iv）降低的能耗; v）增加的冗余。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相同的基站（例如，LTE和5G）。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。该项目涉及研究的本科生和研究生，并在布法罗日期间组织了一次5G和物联网特别会议，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描述移动的网络中的流量，ii）基于强化学习为5G C-RAN制定动态频谱共享模型。这两个目标将遵循3GPP在物联网流量用例和C-RAN规范方面的指导方针。此外，将采用关于物联网设备和网络基站的位置的真实的数据来增加所提出的方法对现实的适应性。从公开来源检索的真实的数据将与随机分布（如泊松点过程）结合用于本研究。将部署一个基于网络的框架，用于收集和共享有关物联网和人类交通的空间和时间数据。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）其分析模型的现实场景，（ii）其网络模拟/仿真研究的输入数据，以及iii）用于其机器的训练数据-该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响进行评估来支持审查标准。

项目成果

Exploring Tradeoffs between Energy Consumption and Network Performance in Cellular-IoT: a Survey

• DOI: 10.1109/globecom46510.2021.9685313
• 发表时间: 2021-12
• 期刊: 2021 IEEE Global Communications Conference (GLOBECOM)
• 作者: Nicholas Accurso;Nicholas Mastronarde;Filippo Malandra

A Layered and Grid-Based Methodology to Characterize and Simulate IoT Traffic on Advanced Cellular Networks

用于表征和模拟高级蜂窝网络上物联网流量的分层且基于网格的方法

• DOI: 10.1109/iotm.001.2200156
• 发表时间: 2023
• 期刊: IEEE Internet of Things Magazine
• 作者: Malandra, Filippo;Mellah, Hakim;Firouzabadi, Abbas Dehghani;Wetté, Constant;Sansò, Brunilde
• 通讯作者: Sansò, Brunilde

Community CBRS Networks - What You Need to Know

社区 CBRS 网络 - 您需要了解的内容

• DOI: 10.1109/fnwf55208.2022.00047
• 发表时间: 2022
• 期刊: IEEE Future Networks World Forum 2022
• 作者: Malandra, Filippo;Silbey, Mari;Alvarez, Rolando;Cacace, Bob;Hege, Troy
• 通讯作者: Hege, Troy

Modelling and Optimization of DRX in Cellular IoT Networks: an MDP Approach

• DOI: 10.1109/icc45041.2023.10279184
• 发表时间: 2023-05
• 期刊: ICC 2023 - IEEE International Conference on Communications
• 作者: Nicholas Accurso;Nicholas Mastronarde;Filippo Malandra

Experimental End-To-End Delay Analysis of LTE Cat-M With High-Rate Synchrophasor Communications

• DOI: 10.1109/jiot.2023.3282268
• 发表时间: 2022-07
• 期刊: IEEE Internet of Things Journal
• 影响因子: 10.6
• 作者: Sureel Shah;Sayan Koley;Filippo Malandra