Similarity Measures for Location-Dependent MMIMO, 5G Base Stations On/Off Switching Using Radio Environment Map

TL;DR

This paper evaluates similarity measures for matching user equipment positions to radio environment patterns in 5G networks, enabling efficient base station switching and up to 56% energy efficiency gains.

Contribution

It introduces a method to select the best similarity metric for matching UE positions to REM data, improving energy efficiency in 5G MMIMO Het-Net networks.

Findings

Sum of Minimums Distance outperforms other metrics in matching accuracy.

Up to 56% energy efficiency improvement achieved.

Effective pattern matching reduces unnecessary base station activation.

Abstract

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.