To Supervise or Not: How to Effectively Learn Wireless Interference Management Models?

TL;DR

This paper provides the first theoretical analysis comparing supervised and unsupervised learning methods for wireless interference management, revealing conditions under which supervised learning outperforms unsupervised approaches and proposing a semi-supervised method.

Contribution

It offers the first theoretical insights into the performance differences between supervised and unsupervised learning in wireless interference management and introduces a semi-supervised approach.

Findings

Supervised learning can outperform unsupervised learning in certain power control problems.

Unsupervised learning may get stuck in low-quality local solutions.

Semi-supervised learning effectively combines both paradigms with limited labels.

Abstract

Machine learning has become successful in solving wireless interference management problems. Different kinds of deep neural networks (DNNs) have been trained to accomplish key tasks such as power control, beamforming and admission control. There are two popular training paradigms for such DNNs-based interference management models: supervised learning (i.e., fitting labels generated by an optimization algorithm) and unsupervised learning (i.e., directly optimizing some system performance measure). Although both of these paradigms have been extensively applied in practice, due to the lack of any theoretical understanding about these methods, it is not clear how to systematically understand and compare their performance. In this work, we conduct theoretical studies to provide some in-depth understanding about these two training paradigms. First, we show a somewhat surprising result, that for some special power control problem, the unsupervised learning can perform much worse than its supervised counterpart, because it is more likely to stuck at some low-quality local solutions. We then provide a series of theoretical results to further understand the properties of the two approaches. Generally speaking, we show that when high-quality labels are available, then the supervised learning is less likely to be stuck at a solution than its unsupervised counterpart. Additionally, we develop a semi-supervised learning approach which properly integrates these two training paradigms, and can effectively utilize limited number of labels to find high-quality solutions. To our knowledge, these are the first set of theoretical results trying to understand different training approaches in learning-based wireless communication system design.