LORM: Learning to Optimize for Resource Management in Wireless Networks with Few Training Samples

TL;DR

This paper introduces LORM, a sample-efficient machine learning framework that optimizes resource management in wireless networks by learning pruning policies, and extends it with transfer learning (LORM-TL) for quick adaptation to new tasks with minimal data.

Contribution

The paper proposes a novel framework LORM that reduces sample complexity and addresses feasibility in resource management, and introduces LORM-TL for rapid transfer learning with minimal unlabeled data.

Findings

LORM outperforms state-of-the-art algorithms in near-optimal performance and speed.

LORM-TL achieves comparable results to models trained with many labeled samples, using only a few unlabeled samples.

The methods effectively handle task mismatch and adapt quickly to new network scenarios.

Abstract

Effective resource management plays a pivotal role in wireless networks, which, unfortunately, results in challenging mixed-integer nonlinear programming (MINLP) problems in most cases. Machine learning-based methods have recently emerged as a disruptive way to obtain near-optimal performance for MINLPs with affordable computational complexity. There have been some attempts in applying such methods to resource management in wireless networks, but these attempts require huge amounts of training samples and lack the capability to handle constrained problems. Furthermore, they suffer from severe performance deterioration when the network parameters change, which commonly happens and is referred to as the task mismatch problem. In this paper, to reduce the sample complexity and address the feasibility issue, we propose a framework of Learning to Optimize for Resource Management (LORM). Instead of the end-to-end learning approach adopted in previous studies, LORM learns the optimal pruning policy in the branch-and-bound algorithm for MINLPs via a sample-efficient method, namely, imitation learning. To further address the task mismatch problem, we develop a transfer learning method via self-imitation in LORM, named LORM-TL, which can quickly adapt a pre-trained machine learning model to the new task with only a few additional unlabeled training samples. Numerical simulations will demonstrate that LORM outperforms specialized state-of-the-art algorithms and achieves near-optimal performance, while achieving significant speedup compared with the branch-and-bound algorithm. Moreover, LORM-TL, by relying on a few unlabeled samples, achieves comparable performance with the model trained from scratch with sufficient labeled samples.