# Uncertainty Injection: A Deep Learning Method for Robust Optimization

## TL;DR

This paper introduces a novel uncertainty injection approach for training deep learning models that enhances robustness against input uncertainties, particularly in wireless communication optimization tasks.

## Contribution

It proposes a new training scheme that implicitly accounts for input uncertainties, improving robustness in optimization problems.

## Key findings

- Effective in robust power loading for MIMO downlink
- Improves power control robustness in D2D networks
- Demonstrates significant performance gains under uncertain conditions

## Abstract

This paper proposes a paradigm of uncertainty injection for training deep learning model to solve robust optimization problems. The majority of existing studies on deep learning focus on the model learning capability, while assuming the quality and accuracy of the inputs data can be guaranteed. However, in realistic applications of deep learning for solving optimization problems, the accuracy of inputs, which are the problem parameters in this case, plays a large role. This is because, in many situations, it is often costly or sometime impossible to obtain the problem parameters accurately, and correspondingly, it is highly desirable to develop learning algorithms that can account for the uncertainties in the input and produce solutions that are robust against these uncertainties. This paper presents a novel uncertainty injection scheme for training machine learning models that are capable of implicitly accounting for the uncertainties and producing statistically robust solutions. We further identify the wireless communications as an application field where uncertainties are prevalent in problem parameters such as the channel coefficients. We show the effectiveness of the proposed training scheme in two applications: the robust power loading for multiuser multiple-input-multiple-output (MIMO) downlink transmissions; and the robust power control for device-to-device (D2D) networks.