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

University Of Southern California, Los Angeles CA

Investigators

Abstract

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.