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

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.