Collaborative Research: CNS Core: Small: Fundamentals of Ultra-Dense Wireless Networks with Generalized Repulsion

合作研究：中枢神经系统核心：小型：具有广义斥力的超密集无线网络的基础

• 批准号: 2006453
• 负责人: Chun-Hung Liu
• 金额: $ 25.5万
• 依托单位: Mississippi State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2006453&HistoricalAwards=false
• 关键词: Collaborative; Research; CNS; Core; Small

An accurate characterization of the statistical behavior of wireless networks is crucial in the analysis, design, and deployment of real-world wireless networks. In the past decade, point processes without spatial repulsion, such as Poisson point processes, have been intensively applied to model and analyze the performances of wireless networks. However, these point processes may not be suitable for modeling and analyzing real-world wireless networks with diverse types of spatial repulsion. This project proposes two families of point processes generalizing various known repulsive processes, which are able to accurately characterize real-world repulsive phenomena in a wireless network such as non-linear and/or asymmetric repulsion. In addition, contrary to the existing results in the literature that are mostly semi-analytical or numerical, the project aims at explicit closed-form characterizations by advanced tools from stochastic geometry and random matrix theory. The results in this project will provide key insights and a new benchmark in the design of various wireless networks.The proposed research resides in the interdisciplinary area of stochastic geometry and wireless networks. The considered point processes are able to characterize diverse nodal repulsion phenomena in ultra-dense wireless networks so as to fundamentally improve the existing modeling and analysis framework of wireless networks with spatial randomness where the impact of node repulsion is ignored. The project has also been motivated by the fact that state-of-art analytical tools in the mathematics community are far from being fully utilized in the wireless networking community. The project aims at the analytical characterization of performance metrics including user association statistics, interference statistics, link rate, and distributed learning, which are based on the closed-form evaluations of some fundamental performance measures. Among other consequences, the proposed research will lead to new scaling laws useful in the deployment of ultra-dense wireless networks for practitioners. The synergy of the PIs brings about the latest ideas and approaches from stochastic geometry and random matrix theory aiming at new breakthroughs in understanding the fundamental behavior of wireless networks. The outcome of the proposed research also has applications in other domains, such as in machine learning and data science, where the project results offer new algorithms for sampling, marginalization, conditioning, and other inference tasks.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.

准确描述无线网络的统计行为对于实际无线网络的分析、设计和部署至关重要。在过去的十年中，无空间斥力的点过程，如泊松点过程，被广泛地应用于无线网络的建模和性能分析。然而，这些点过程可能不适合对具有不同类型空间排斥的真实世界无线网络进行建模和分析。这个项目提出了两类点过程，它们概括了各种已知的排斥过程，能够准确地描述无线网络中的真实世界排斥现象，如非线性和/或非对称排斥。此外，与现有文献中大多是半解析或数值的结果相反，该项目旨在利用随机几何和随机矩阵理论的高级工具来显式地描述闭合形式。该项目的结果将为各种无线网络的设计提供关键的见解和新的基准。拟议的研究位于随机几何和无线网络的交叉领域。所考虑的点过程能够刻画超密集无线网络中不同的节点排斥现象，从而从根本上改善现有的忽略节点排斥影响的空间随机性无线网络的建模和分析框架。推动该项目的另一个原因是，数学界最先进的分析工具远未在无线网络社区得到充分利用。该项目旨在对性能指标进行分析表征，包括用户关联统计、干扰统计、链路速率和分布式学习，这些指标基于对一些基本性能指标的封闭形式评估。除了其他后果外，拟议的研究将导致新的比例法则，对从业者部署超高密度无线网络有用。PI的协同作用带来了随机几何和随机矩阵理论的最新思想和方法，目的是在理解无线网络基本行为方面取得新的突破。拟议的研究成果在其他领域也有应用，如在机器学习和数据科学中，项目结果为采样、边缘化、条件化和其他推理任务提供了新的算法。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势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

Ultra-Reliable and Low-Latency Communications Using Proactive Multi-Cell Association

• DOI: 10.1109/tcomm.2021.3065979
• 发表时间: 2021-06-01
• 期刊: IEEE TRANSACTIONS ON COMMUNICATIONS
• 影响因子: 8.3
• 作者: Liu, Chun-Hung;Liang, Di-Chun;Gau, Rung-Hung
• 通讯作者: Gau, Rung-Hung

Spatio-Temporal Federated Learning for Massive Wireless Edge Networks

• DOI: 10.1109/icc45855.2022.9838401
• 发表时间: 2021-10
• 期刊: ICC 2022 - IEEE International Conference on Communications
• 作者: Chun-Hung Liu;Kai-Ten Feng;Luwei Wei;Yung-Jie Luo

Modeling and Analysis of Intermittent Federated Learning Over Cellular-Connected UAV Networks

蜂窝连接无人机网络间歇性联邦学习的建模和分析

• DOI: 10.1109/vtc2022-spring54318.2022.9860913
• 发表时间: 2022
• 期刊: IEEE Vehicular Technology Conference
• 作者: Liu, Chun-Hung;Liang, Di-Chun;Gau, Rung-Hung;Wei, Lu
• 通讯作者: Wei, Lu

Wireless Networked Multirobot Systems in Smart Factories

• DOI: 10.1109/jproc.2020.3033753
• 发表时间: 2021-04-01
• 期刊: PROCEEDINGS OF THE IEEE
• 影响因子: 20.6
• 作者: Chen, Kwang-Cheng;Lin, Shih-Chun;Fettweis, Gerhard P.
• 通讯作者: Fettweis, Gerhard P.

Federated Deep Reinforcement Learning for THz-Beam Search with Limited CSI

• DOI: 10.1109/vtc2022-fall57202.2022.10012887
• 发表时间: 2022-09
• 期刊: 2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall)
• 作者: Po-chun Hsu;Li-Hsiang Shen;Chun-Hung Liu;Kai-Ten Feng