Adversarial Training: Enhancing Out-of-Distribution Generalization for Learning Wireless Resource Allocation

TL;DR

This paper introduces an adversarial training approach to improve the out-of-distribution generalization of deep neural networks in wireless resource allocation, demonstrating significant performance gains across different channel conditions.

Contribution

It proposes a novel offline unsupervised adversarial training method tailored for enhancing DNNs' robustness to distribution shifts in wireless environments.

Findings

5-20% improvement in OOD performance

Effective with limited training distributions

Enhanced robustness across various channel types

Abstract

Unsupervised learning has been extensively adopted to train deep neural networks (DNNs) for learning wireless resource allocation. Yet, the performance of DNNs is vulnerable to distribution shifts between training and test data, e.g., wireless channels. In this paper, we propose an offline unsupervised training method to enhance the out-of-distribution (OOD) generalizability of DNNs. Inspired by adversarial training (AT), the method trains DNNs using progressively identified adversarial examples out of the training distribution. To reflect OOD degradation of a DNN in the context of unsupervised learning, we reformulate the optimization problem of AT. The proposed method is evaluated by learning hybrid precoding. Simulation results showcase the enhanced OOD performance of multiple kinds of DNNs with approximately 5\(\sim\)20\% improvement across various channel distributions, even when the samples only from a single distribution (e.g., Rayleigh fading) are used for training.