  The Massive Multiple-Input Multiple-Output (MMIMO) technique together with
Heterogeneous Network (Het-Net) deployment enables high throughput of 5G and
beyond networks. However, a high number of antennas and a high number of Base
Stations (BSs) can result in significant power consumption. Previous studies
have shown that the energy efficiency (EE) of such a network can be effectively
increased by turning off some BSs depending on User Equipments (UEs) positions.
Such mapping is obtained by using Reinforcement Learning. Its results are
stored in a so-called Radio Environment Map (REM). However, in a real network,
the number of UEs' positions patterns would go to infinity. This paper aims to
determine how to match the current set of UEs' positions to the most similar
pattern, i.e., providing the same optimal active BSs set, saved in REM. We
compare several state-of-the-art distance metrics using a computer simulator:
an accurate 3D-Ray-Tracing model of the radio channel and an advanced
system-level simulator of MMIMO Het-Net. The results have shown that the
so-called Sum of Minimums Distance provides the best matching between REM data
and UEs' positions, enabling up to 56% EE improvement over the scenario without
EE optimization.
