NeTS: Small: Toward Optimal, Efficient, and Holistic Networking Design for Massive-MIMO Wireless Networks

NeTS：小型：面向大规模 MIMO 无线网络的优化、高效和整体网络设计

• 批准号: 2102233
• 负责人: Jia Liu
• 金额: $ 13.33万
• 依托单位: Ohio 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: 2020-08-16 至 2021-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2102233&HistoricalAwards=false
• 关键词: NeTS; Small; Toward; Optimal; Efficient

The proliferation of mobile-Internet devices in recent years (e.g., smartphones, tablets, etc.) has continuously pushed the current wireless networks to their capacity limits. A key approach to enlarge network capacity is to increase spectrum efficiency. To allow future wireless networks to support multi-Gigabit per second data rates, there have been significant research efforts in recent years termed 'massive multiple-input multiple-output' systems (M-MIMO), where the numbers of antennas are scaled up to hundreds or even thousands. However, so far, these efforts are mostly limited to problems at the physical layer or signal processing aspects. There is very limited knowledge on how to optimally employ M-MIMO technologies and develop control and optimization algorithms to improve network capacity, delay, and energy expenditure performances from networking perspectives. The proposed research will enable the networking community by facilitating the development of M-MIMO-based wireless networks with substantially increased network performances in terms of throughput, delay, energy expenditure. Through this project, the investigator also aims to advance M-MIMO research in formal classroom instruction and educate students to inculcate a strong comprehension and knowledge of M-MIMO. The investigator will host a workshop to present research findings to companies in the areas of wireless networking. This research will not only advance the knowledge in the design of M-MIMO wireless networks but will also serve a critical need in the general networking research community by exploring a network-level understanding of M-MIMO networks through a unified research program, which consists of the development of tractable cross-layer theoretical models, exploration of theoretical performance bounds and limits, and the development of distributed algorithms.This project strives to answer the following overarching questions: Could the linear capacity scaling law at the M-MIMO physical layer be translated into upper layers' performance gains in throughput, delay, and energy expenditure? Specifically, the project will focus the research efforts along the following three interrelated thrust areas: 1) optimal routing and congestion control for M-MIMO multi-hop backhaul networks; 2) efficient scheduling design for M-MIMO cellular networks; and 3) energy analytics for M-MIMO wireless networks. The project aims to bridge the gaps between physical layer advances in M-MIMO and wireless networking research.

近年来移动互联网设备（例如智能手机、平板电脑等）的激增不断将当前无线网络的容量推向极限。扩大网络容量的一个关键方法是提高频谱效率。为了让未来的无线网络支持每秒数千兆位的数据速率，近年来人们进行了大量的研究工作，称为“大规模多输入多输出”系统（M-MIMO），其中天线的数量扩大到数百甚至数千。然而，到目前为止，这些努力大多局限于物理层或信号处理方面的问题。关于如何优化使用 M-MIMO 技术以及开发控制和优化算法以从网络角度提高网络容量、延迟和能量消耗性能的知识非常有限。拟议的研究将通过促进基于 M-MIMO 的无线网络的开发来支持网络社区，并在吞吐量、延迟和能量消耗方面显着提高网络性能。通过这个项目，研究人员还旨在推进正式课堂教学中的 M-MIMO 研究，并教育学生灌输对 M-MIMO 的深刻理解和知识。研究人员将举办一个研讨会，向无线网络领域的公司展示研究成果。这项研究不仅将推进 M-MIMO 无线网络设计的知识，而且还将通过统一的研究计划探索对 M-MIMO 网络的网络级理解，从而满足一般网络研究社区的关键需求，该计划包括该项目致力于回答以下首要问题：M-MIMO 物理层的线性容量缩放定律是否可以转化为上层在吞吐量、延迟、和能量消耗？ 具体来说，该项目将集中研究以下三个相互关联的重点领域：1）M-MIMO多跳回程网络的最优路由和拥塞控制； 2）M-MIMO蜂窝网络的高效调度设计； 3) M-MIMO 无线网络的能量分析。该项目旨在弥合 M-MIMO 物理层进展和无线网络研究之间的差距。

项目成果

Achieving Information Freshness With Selfish and Rational Users in Mobile Crowd-Learning

在移动众学习中实现自私理性用户的信息新鲜度

• DOI: 10.1109/jsac.2021.3065092
• 发表时间: 2021-05
• 期刊: IEEE Journal on Selected Areas in Communications
• 影响因子: 16.4
• 作者: Li, Bin;Liu, Jia
• 通讯作者: Liu, Jia

ARA: A Wireless Living Lab Vision for Smart and Connected Rural Communities

ARA：智能互联农村社区的无线生活实验室愿景

• DOI: 10.1145/3477086.3480837
• 发表时间: 2021-10
• 期刊: WINTECH 2021
• 作者: Zhang, Hongwei;Guan, Yong;Kamal, Ahmed;Qiao, Daji;Zheng, Mai;Arora, Anish;Boyraz, Ozdal;Cox, Brian;Daniels, Thomas;Darr, Matthew;et al
• 通讯作者: et al

"Taming Communication and Sample Complexities in Decentralized Policy Evaluation for Cooperative Multi-Agent Reinforcement Learning

“在协作多智能体强化学习的去中心化政策评估中驯服通信和样本复杂性

• 发表时间: 2021-01
• 期刊: Proc. NeurIPS
• 作者: Zhang, Xin;Liu, Zhuqing;Liu, Jia;Zhu, Zhengyuan;Lu, Songtao
• 通讯作者: Lu, Songtao

Low-Overhead Wireless Uplink Scheduling for Large-Scale Internet-of-Things

大规模物联网的低开销无线上行链路调度

• 发表时间: 2021-02
• 期刊: IEEE transactions on mobile computing
• 影响因子: 7.9
• 作者: Li, Bin;Liu, Jia;Ji, Bo
• 通讯作者: Ji, Bo

STEM: A Stochastic Two-Sided Momentum Algorithm Achieving Near-Optimal Sample and Communication Complexities for Federated Learning

STEM：一种随机双边动量算法，为联邦学习实现接近最优的样本和通信复杂性

• 发表时间: 2021-06-19
• 期刊: ArXiv
• 作者: Prashant Kh;uri;uri;Pranay Sharma;Haibo Yang;Min;Jia Liu;K. Rajawat;P. Varshney
• 通讯作者: P. Varshney