  This paper studies practical limitations of learning methods for resource
management in non-stationary radio environment. We propose two learning models
carefully designed to support rate maximization objective under user mobility.
We study the effects of practical systems such as latency and reliability on
the rate maximization with deep learning models. For common testing in the
non-stationary environment we present a generic dataset generation method to
benchmark across different learning models versus traditional optimal resource
management solutions. Our results indicate that learning models have practical
challenges related to training limiting their applications. The models need
environment-specific design to reach the accuracy of an optimal algorithm.
