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: 1758757
• 负责人: Jia Liu
• 金额: $ 28.75万
• 依托单位: Iowa State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-16 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1758757&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Internet设备的扩散（例如智能手机，平板电脑等）不断将当前的无线网络推向其容量限制。扩大网络容量的关键方法是提高频谱效率。为了允许未来的无线网络支持每秒数据速率的多gabit，近年来，已经进行了重大的研究工作，称为“大规模多输入多输出”系统（M-MIMO），其中天线的数量最高可达数百甚至数千个。但是，到目前为止，这些努力大多仅限于物理层或信号处理方面的问题。关于如何最佳采用M-MIMO技术并开发控制和优化算法以从网络角度提高网络容量，延迟和能源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，在吞吐量，延迟，能源消耗方面具有大大增加的网络性能。通过该项目，研究人员还旨在推进正式课堂教学中的M-Mimo研究，并教育学生对M-Mimo的强烈理解和知识进行灌输。研究人员将举办一个研讨会，向无线网络领域的公司介绍研究结果。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网络级别对M-MIMO网络的了解，在一般网络研究社区中满足了至关重要的需求，该计划包括开发可交叉的跨层次理论模型，探索理论绩效和限制的问题，以及分布式的ALGORSSSSSS，以下问题： M-MIMO物理层处的线性容量缩放定律可以转化为上层的吞吐量，延迟和能量消耗的上层性能增长？ 具体而言，该项目将集中于以下三个相互关联的推力领域的研究工作：1）M-MIMO多跳回程网络的最佳路由和拥塞控制； 2）M-MIMO蜂窝网络的有效调度设计； 3）M-MIMO无线网络的能源分析。该项目旨在弥合M-MIMO和无线网络研究中物理层进展之间的差距。

项目成果

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Exploring Best Arm with Top Reward-Cost Ratio in Stochastic Bandits

• DOI: 10.1109/infocom41043.2020.9155362
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• 期刊: IEEE INFOCOM 2020 - IEEE Conference on Computer Communications
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Communication-Efficient Network-Distributed Optimization with Differential-Coded Compressors

• DOI: 10.1109/infocom41043.2020.9155432
• 发表时间: 2019-12
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The Sample Complexity of Best-k Items Selection from Pairwise Comparisons

• 发表时间: 2020-07
• 作者: Wenbo Ren;Jia Liu;N. Shroff

MATE: A Memory-Augmented Time-Expansion Approach for Optimal Trip-Vehicle Matching and Routing in Ride-Sharing

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• DOI: 10.1145/3396851.3397726
• 发表时间: 2020
• 期刊: Proc. ACM e-Energy
• 作者: Tian, Ye;Liu, Jia;Xia, Cathy H.
• 通讯作者: Xia, Cathy H.