NeTS:Small:Optimal Learning Times for Task-Oriented Communication Networks

NeTS:Small：面向任务的通信网络的最佳学习时间

基本信息

批准号：
1718477
负责人：
Michael Neely
金额：
$ 47.75万
依托单位：
University of Southern California
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：
2017-09-01 至 2022-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1718477&HistoricalAwards=false
关键词：
NeTS Small Optimal Learning Times

项目摘要

Communication networks must support a diverse set of tasks while quickly adapting to time-varying conditions. This project seeks to develop and characterize fast and adaptive network control methods. The methods respect the energy, computation, communication, and sensing resources of the network while maintaining high quality execution of each task and providing fair sharing across all users. The goal is to create smarter networks with faster response times and lower delays while adapting to changes in user mobility and device functionality. The research has broad applications to control theory, operations research, smart grid scheduling, economics, and game theory. This project will also train several graduate students on these topics.This work is challenging because of time variation, mobility, and asynchronous start and stop times of each task. The fundamental optimization problems involve nonconvex ratios of time averages that have not been significantly explored. The optimal convergence times are unknown. Preliminary work of the principal investigator develops a new method for improving convergence time of subgradient-based convex programming. This project seeks to extend this method and to develop new methods for solving the more difficult problems of task-oriented stochastic networking. Another component of this work is the development of fundamental lower bounds on convergence times in this context. This can have a high impact on broader areas of optimization, stochastic control, online decision making, and machine learning.

通信网络必须支持各种各样的任务，同时快速适应时间变化的条件。该项目旨在开发和表征快速和自适应网络控制方法。这些方法尊重网络的能源，计算，通信和感测资源，同时维持每个任务的高质量执行并在所有用户中提供公平共享。目的是创建具有更快响应时间和较低延迟的智能网络，同时适应用户移动性和设备功能的变化。该研究在控制理论，运营研究，智能网格计划，经济学和游戏理论方面有广泛的应用。该项目还将在这些主题上培训几位研究生。由于时间变化，流动性和异步开始和停止时间，这项工作具有挑战性。基本优化问题涉及尚未显着探索的时间平均值的非凸比。最佳收敛时间未知。主要研究者的初步工作开发了一种新方法，用于改善基于亚级别的凸面编程的收敛时间。该项目旨在扩展此方法并开发新方法，以解决面向任务的随机网络的更困难的问题。这项工作的另一个组成部分是在这种情况下开发了融合时间的基本下限。这可能会对更广泛的优化，随机控制，在线决策和机器学习产生很大影响。