NeTS: Small: Collaborative Research: Improving Spectrum Efficiency for Hyper-Dense IoT Networks

NeTS：小型：协作研究：提高超密集物联网网络的频谱效率

• 批准号: 1815637
• 负责人: Xiaoli Ma
• 金额: $ 17.5万
• 依托单位: Georgia Tech Research Corporation
• 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 2022-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1815637&HistoricalAwards=false
• 关键词: NeTS; Small; Collaborative; Research; Improving

The emerging Internet of Things (IoT) technology will enable a whole new set of applications, imposing far reaching influence on many aspects of society, including management of health, transportation, agriculture, safety and emergency and disaster response. At the same time, the massive growth of IoT deployments poses several grand challenges to the wireless industry for their successful operation. This project aims to develop novel IoT architectures and algorithms to enable large-scale IoT deployments. In particular, through close collaboration of three academic institutions and industry, the proposed research will make advances by introducing holistic, cross-layer design framework and protocols for improving spectral/energy efficiency and latency of massive IoT networks. The research outcomes will support emerging standardization activities to enable IoT in next-generation wireless networks. This project aims at tackling the following challenges: 1) System-Centric Waveform Design for Massive IoT: The researchers will couple stochastic geometry techniques and ambiguity functions of carrier waveforms to perform system-wide capacity analysis, compare waveform candidates for deployment suitability, develop joint equalizer and waveform designs for massive IoT deployments, and propose new waveforms having a good spectral efficiency, interference resilience, and latency characteristics. 2) Graph-based Radio Resource Management: Judicious radio resource management is critical to the proper functioning of hyper-dense IoT networks. The project will exploit weighted partitioning and matching tools from graph theory to develop waveform-aware dynamic resource allocation schemes to suppress mutual interference among a diverse set of wireless IoT links and improve spectrum utilization efficiency in single-hop and multi-hop massive IoT deployments. 3) Core network Connection Efficiency: Rethinking the core network functions for connection of massive IoT is required as they are not designed considering the IoT traffic characteristics. The project will develop and rigorously study an aggregation based connectivity model to manage multiple IoT device traffic using the same resources (i.e., bearers). For efficient groupings of the IoT devices that will share the same connection, researchers will use efficient evolutionary clustering methods, while giving preference to the interference minimizing groups obtained in earlier thrusts.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.

新兴的物联网（IoT）技术将带来一系列全新的应用，对社会的许多方面产生深远的影响，包括健康管理、交通、农业、安全以及应急和灾害响应。与此同时，物联网部署的大规模增长为无线行业的成功运营带来了几个重大挑战。该项目旨在开发新的物联网架构和算法，以实现大规模物联网部署。特别是，通过三个学术机构和行业的密切合作，拟议的研究将通过引入整体的跨层设计框架和协议来提高频谱/能源效率和大规模物联网网络的延迟。研究成果将支持新兴的标准化活动，使物联网在下一代无线网络中。该项目旨在解决以下挑战：1）面向大规模物联网的以系统为中心的波形设计：研究人员将结合随机几何技术和载波波形的模糊函数来进行系统范围的容量分析，比较部署适用性的候选波形，为大规模物联网部署开发联合均衡器和波形设计，并提出具有良好频谱效率，抗干扰能力，和延迟特性。2)基于图的无线电资源管理：明智的无线电资源管理对于超密集物联网网络的正常运行至关重要。该项目将利用图论中的加权划分和匹配工具来开发波形感知的动态资源分配方案，以抑制各种无线物联网链路之间的相互干扰，并提高单跳和多跳大规模物联网部署的频谱利用效率。3)核心网络连接效率：需要重新考虑连接大规模物联网的核心网络功能，因为它们没有考虑物联网流量特性。该项目将开发并严格研究基于聚合的连接模型，以管理使用相同资源的多个物联网设备流量（即，bearers）。为了将共享同一连接的物联网设备高效分组，研究人员将使用高效的进化聚类方法，同时优先考虑在早期研究中获得的干扰最小化组。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

Deep Over-the-Air Computation

• DOI: 10.1109/globecom42002.2020.9321975
• 发表时间: 2020-12
• 期刊: GLOBECOM 2020 - 2020 IEEE Global Communications Conference
• 作者: Hao Ye;Geoffrey Y. Li;B. Juang

Deep Learning Based End-to-End Wireless Communication Systems Without Pilots

• DOI: 10.1109/tccn.2021.3061464
• 发表时间: 2021-02
• 期刊: IEEE Transactions on Cognitive Communications and Networking
• 影响因子: 8.6
• 作者: Hao Ye;Geoffrey Y. Li;B. Juang