# Control-Aware Scheduling for Low Latency Wireless Systems with Deep Learning

## TL;DR

This paper introduces a deep learning-based control-aware scheduler for low-latency wireless systems, optimizing transmission timing based on control and channel states without needing explicit system models.

## Contribution

It formulates a novel constrained optimization problem for scheduling, solved via primal-dual learning with deep neural networks, enabling model-free adaptation to control system demands.

## Key findings

- Outperforms heuristic scheduling methods in simulations
- Does not require explicit channel or system models
- Effective in low-latency control scenarios

## Abstract

We consider the problem of scheduling transmissions over low-latency wireless communication links to control various control systems. Low-latency requirements are critical in developing wireless technology for industrial control and Tactile Internet, but are inherently challenging to meet while also maintaining reliable performance. An alternative to ultra reliable low latency communications is a framework in which reliability is adapted to control system demands. We formulate the control-aware scheduling problem as a constrained statistical optimization problem in which the optimal scheduler is a function of current control and channel states. The scheduler is parameterized with a deep neural network, and the constrained problem is solved using techniques from primal-dual learning, which have a necessary model-free property in that they do not require explicit knowledge of channels models, performance metrics, or system dynamics to execute. The resulting control-aware deep scheduler is evaluated in empirical simulations and strong performance is shown relative to other model-free heuristic scheduling methods.