  In wireless communication systems (WCSs), the network optimization problems
(NOPs) play an important role in maximizing system performances by setting
appropriate network configurations. When dealing with NOPs by using
conventional optimization methodologies, there exist the following three
problems: human intervention, model invalid, and high computation complexity.
As such, in this article we propose an auto-learning framework (ALF) to achieve
intelligent and automatic network optimization by using machine learning (ML)
techniques. We review the basic concepts of ML techniques, and propose their
rudimentary employment models in WCSs, including automatic model construction,
experience replay, efficient trial-and-error, RL-driven gaming, complexity
reduction, and solution recommendation. We hope these proposals can provide new
insights and motivations in future researches for dealing with NOPs in WCSs by
using ML techniques.
