A Fundamental Study on Energy Efficient Wireless Communication Networks: Modeling, Algorithms, and Applications

节能无线通信网络的基础研究:建模、算法和应用

基本信息

  • 批准号:
    1610874
  • 负责人:
  • 金额:
    $ 38万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2016
  • 资助国家:
    美国
  • 起止时间:
    2016-08-01 至 2020-07-31
  • 项目状态:
    已结题

项目摘要

The rapid development and wide deployment of wireless networks incur a fast escalation of energy demand, which eagerly calls for energy-efficient networking techniques. At the same time, wireless networks are evolving into complex forms with multi-dimensional resources including communication link, radio, channel, antenna, and transmit power; algorithms with low complexity for energy efficiency optimization are highly demanded. This project targets at a fundamental study on energy-efficient wireless networking through establishment of a uniformed analytical framework, development of efficient and low-complexity algorithms, and application of the generic studies into important scenarios in the fifth generation (5G) cellular systems. This interdisciplinary research will not only provide various training projects to undergraduate and graduate studies, but also inspire students to pursue high-quality research with a creative, open-minded, and cross-disciplinary perspective. This project is going to demonstrate that a uniformed multidimensional optimization framework for energy efficiency optimization can be constructed by the principle of scheduling proper transmission patterns, which are defined by the interference model of the network. With such a uniformed optimization model, low-complexity decomposition techniques are fundamentally related to a maximum weighted transmission pattern (MWTP) problem, under a physical interference model according to the signal-to-interference-plus-noise ratio. Approximation algorithms and associated performance analysis for the MWTP problem (which is NP-hard in general) are critical research issues to be studied. Distributed algorithms for solving the energy efficiency optimization problem further involves decomposition with multi-objective optimization, and innovative Lyapunov function design and associated stability analysis under the physical interference model, which will also be addressed in this project. Energy efficient solutions in a couple of important 5G cellular scenarios will be enabled through innovative modeling and algorithms in the uniformed multidimensional framework, including joint optimization that incorporates massive MIMO interference mitigation with flow constraint at network layer and base station sleeping at system level, formulation and algorithm development for a MWTP problem under the massive MIMO interference model, and modeling of the interplay between massive MIMO and device-to-device communications. In this project, the proposed research seamlessly integrates studies in the areas of optimization, graph theory, dual decomposition, approximation algorithms, and wireless communication and networking. The research outcomes are expected to provide important guidance for the development of the 5G cellular systems.
无线网络的快速发展和广泛部署导致了能源需求的快速增长,这迫切需要节能的网络技术。与此同时,无线网络正在演变成具有多维资源(包括通信链路、无线电、信道、天线和发射功率)的复杂形式,因此高度要求用于能量效率优化的低复杂度算法。该项目旨在通过建立统一的分析框架,开发高效和低复杂度的算法,并将通用研究应用于第五代(5G)蜂窝系统的重要场景,对节能无线网络进行基础研究。这种跨学科的研究不仅将提供各种培训项目,以本科和研究生的学习,但也激励学生追求高质量的研究与创造性,开放的思想,跨学科的角度。该项目将证明,一个统一的多维优化框架的能源效率优化可以构建的原则,调度适当的传输模式,这是由网络的干扰模型定义。利用这种统一的优化模型,低复杂度分解技术基本上与根据信号与干扰加噪声比的物理干扰模型下的最大加权传输模式(MWTP)问题相关。MWTP问题的近似算法和相关的性能分析(这是NP-难的一般)是关键的研究问题进行研究。解决能源效率优化问题的分布式算法进一步涉及多目标优化分解,以及创新的李雅普诺夫函数设计和物理干扰模型下的相关稳定性分析,这些也将在本项目中讨论。通过统一多维框架中的创新建模和算法,将在几个重要的5G蜂窝场景中实现节能解决方案,包括在网络层将大规模MIMO干扰缓解与流量约束结合起来的联合优化以及在系统级的基站睡眠,大规模MIMO干扰模型下MWTP问题的公式化和算法开发,以及对大规模MIMO和设备到设备通信之间的相互作用进行建模。在这个项目中,所提出的研究无缝地集成了优化,图论,对偶分解,近似算法,无线通信和网络等领域的研究。 研究成果有望为5G蜂窝系统的发展提供重要指导。

项目成果

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Yu Cheng其他文献

Precision enhancement of three-dimensional displacement tracing for nano-fabrication based on low coherence interferometry
基于低相干干涉技术的纳米加工三维位移追踪精度提升
  • DOI:
    10.1364/oe.27.028324
  • 发表时间:
    2019
  • 期刊:
  • 影响因子:
    3.8
  • 作者:
    Yu Cheng;Xiangchao Zhang;He Yuan;Wei Wang;Min Xu
  • 通讯作者:
    Min Xu
Anti-inflammatory effect of Yu-Ping-Feng-San via TGF-β1 signaling suppression in rat model of COPD
玉屏风散通过抑制 TGF-β1 信号传导抑制 COPD 大鼠模型的抗炎作用
  • DOI:
  • 发表时间:
    2016
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Zhong-Shan Yang;Jin-Yuan Yan;Ni-Ping Han;Wei Zhou;Yu Cheng;Xiao-Mei Zhang;Ning Li;Jia-Li Yuan
  • 通讯作者:
    Jia-Li Yuan
Preparation and catalytic performance of N-[(2-Hydroxy-3-trimethylammonium) propyl] chitosan chloride /Na2SiO3 polymer-based catalyst for biodiesel production
N-[(2-羟基-3-三甲基铵)丙基]氯化壳聚糖/Na2SiO3聚合物基生物柴油催化剂的制备及催化性能
  • DOI:
    10.1016/j.renene.2015.11.036
  • 发表时间:
    2016-04
  • 期刊:
  • 影响因子:
    8.7
  • 作者:
    BenQiao He;YiXuan Shao;JianXin Li;Yu Cheng
  • 通讯作者:
    Yu Cheng
Object tracking in the complex environment based on SIFT
基于SIFT的复杂环境目标跟踪
A Neutrophil-Inspired Supramolecular Nanogel for Magnetocaloric-Enzymatic Tandem Therapy
用于磁热酶串联疗法的中性粒细胞启发的超分子纳米凝胶
  • DOI:
  • 发表时间:
    2020
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Qi Zhang;Jiaojiao Wu;Jingjing Wang;Xia Wang;Chu Wu;Mengwei Chen;Qing Wu;Maciej S. Lesniak;Yongli Mi;Yu Cheng;Qigang Wang
  • 通讯作者:
    Qigang Wang

Yu Cheng的其他文献

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{{ truncateString('Yu Cheng', 18)}}的其他基金

AF: Small: Faster Algorithms for High-Dimensional Robust Statistics
AF:小:用于高维稳健统计的更快算法
  • 批准号:
    2122628
  • 财政年份:
    2022
  • 资助金额:
    $ 38万
  • 项目类别:
    Standard Grant
AF: Small: Faster Algorithms for High-Dimensional Robust Statistics
AF:小:用于高维稳健统计的更快算法
  • 批准号:
    2307106
  • 财政年份:
    2022
  • 资助金额:
    $ 38万
  • 项目类别:
    Standard Grant
CNS Core: Small: Application-Oriented Scheduling for Optimizing Information Freshness in Wireless Networks
CNS 核心:小型:面向应用的调度,用于优化无线网络中的信息新鲜度
  • 批准号:
    2008092
  • 财政年份:
    2020
  • 资助金额:
    $ 38万
  • 项目类别:
    Standard Grant
Dynamic Multivariate Normative Comparison and Risk Screening for Alzheimer's Disease Progression
阿尔茨海默病进展的动态多变量规范比较和风险筛查
  • 批准号:
    1916001
  • 财政年份:
    2019
  • 资助金额:
    $ 38万
  • 项目类别:
    Standard Grant
NeTS: Small: Machine Learning Meets Wireless Network Optimization: Exploring the Latent Knowledge
NeTS:小型:机器学习遇见无线网络优化:探索潜在知识
  • 批准号:
    1816908
  • 财政年份:
    2018
  • 资助金额:
    $ 38万
  • 项目类别:
    Standard Grant
NSF Student Travel Grant for 2016 IEEE Global Communications Conference (IEEE GLOBECOM)
2016 年 IEEE 全球通信会议 (IEEE GLOBECOM) 的 NSF 学生旅费补助
  • 批准号:
    1643335
  • 财政年份:
    2016
  • 资助金额:
    $ 38万
  • 项目类别:
    Standard Grant
NeTS: Small: Collaborative Research: Towards Reliable, Energy-Efficient, and Secure Vehicular Networks
NetS:小型:协作研究:迈向可靠、节能和安全的车辆网络
  • 批准号:
    1320736
  • 财政年份:
    2014
  • 资助金额:
    $ 38万
  • 项目类别:
    Standard Grant
Association, Regression and Diagnostic Accuracy Analyses of Competing Risks Data
竞争风险数据的关联、回归和诊断准确性分析
  • 批准号:
    1207711
  • 财政年份:
    2012
  • 资助金额:
    $ 38万
  • 项目类别:
    Standard Grant
TC: Small: Real-Time Intrusion Detection for VoIP over IEEE 802.11 Based Wireless Networks: An Analytical Approach for Guaranteed Performance
TC:小型:基于 IEEE 802.11 的无线网络的 VoIP 实时入侵检测:保证性能的分析方法
  • 批准号:
    1117687
  • 财政年份:
    2012
  • 资助金额:
    $ 38万
  • 项目类别:
    Continuing Grant
CAREER: Exploring the Underexplored: A Fundamental Study of Optimal Resource Allocation and Low-Complexity Algorithms in Multi-Radio Multi-Channel Wireless Networks
职业:探索未开发领域:多无线电多通道无线网络中最优资源分配和低复杂度算法的基础研究
  • 批准号:
    1053777
  • 财政年份:
    2011
  • 资助金额:
    $ 38万
  • 项目类别:
    Continuing Grant

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