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

通信网络必须支持多种任务，同时快速适应时变条件。该项目旨在开发和表征快速和自适应的网络控制方法。这些方法尊重网络的能源、计算、通信和传感资源，同时保持每个任务的高质量执行，并在所有用户之间提供公平的共享。目标是创建具有更快响应时间和更低延迟的智能网络，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、运筹学、智能电网调度、经济学、博弈论等领域有着广泛的应用。这个项目也将训练一些关于这些主题的研究生。由于时间变化、移动性和每个任务的异步启动和停止时间，这项工作具有挑战性。基本的优化问题涉及时间平均值的非凸比，这还没有得到显著的探索。最优收敛时间未知。主要研究人员的初步工作是提出一种改进基于次梯度的凸规划收敛时间的新方法。本项目旨在扩展这种方法，并开发新的方法来解决面向任务的随机网络中更困难的问题。这项工作的另一个组成部分是在这种情况下收敛时间的基本下界的发展。这可能对优化、随机控制、在线决策和机器学习等更广泛的领域产生重大影响。

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.