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

• URL: http://arxiv.org/abs/2112.14011v1
• Date: Tue, 28 Dec 2021 06:29:08 GMT
• Title: To Supervise or Not: How to Effectively Learn Wireless Interference Management Models?
• Abstract summary: Machine learning has become successful in solving wireless interference management problems. There are two popular training paradigms for such interference management models: supervised learning and unsupervised learning. We show that for some special power control problem, the unsupervised learning can perform much worse than its supervised counterpart.
• Score: 43.317056712257994
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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