# Increasing Energy Efficiency of Massive-MIMO Network via Base Stations Switching using Reinforcement Learning and Radio Environment Maps

arXiv: https://arxiv.org/abs/2103.11891 · HTML: https://mlsanity.com/paper/2103.11891/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 44 references that are in the map.
- First posted on arXiv 8 Mar 2021.

## Builds on (most cited first, 4 of 4)

- [DRAG: Deep Reinforcement Learning Based Base Station Activation in Heterogeneous Networks](https://mlsanity.com/paper/1809.02159/) (2018)
- [Spatial Spectrum and Energy Efficiency of Random Cellular Networks](https://mlsanity.com/paper/1501.05368/) (2015)
- [Energy Efficiency in Massive MIMO-Based 5G Networks: Opportunities and Challenges](https://mlsanity.com/paper/1511.08689/) (2015)
- [Energy Efficiency Optimization of 5G Radio Frequency Chain Systems](https://mlsanity.com/paper/1604.02665/) (2016)

## Built on by (most cited first, 3 of 3)

- [VREM-FL: Mobility-Aware Computation-Scheduling Co-Design for Vehicular Federated Learning](https://mlsanity.com/paper/2311.18741/) (2023)
- [Detecting Malicious Pilot Contamination in Multiuser Massive MIMO Using Decision Trees](https://mlsanity.com/paper/2510.03831/) (2025)
- [Energy Efficient Sleep Mode Optimization in 5G mmWave Networks via Multi Agent Deep Reinforcement Learning](https://mlsanity.com/paper/2511.22105/) (2025)

## Abstract (arXiv)

Energy Efficiency (EE) is of high importance while considering Massive Multiple-Input Multiple-Output (M-MIMO) networks where base stations (BSs) are equipped with an antenna array composed of up to hundreds of elements. M-MIMO transmission, although highly spectrally efficient, results in high energy consumption growing with the number of antennas. This paper investigates EE improvement through switching on/off underutilized BSs. It is proposed to use the location-aware approach, where data about an optimal active BSs set is stored in a Radio Environment Map (REM). For efficient acquisition, processing and utilization of the REM data, reinforcement learning (RL) algorithms are used. State-of-the-art exploration/exploitation methods including e-greedy, Upper Confidence Bound (UCB), and Gradient Bandit are evaluated. Then analytical action filtering, and an REM-based Exploration Algorithm (REM-EA) are proposed to improve the RL convergence time. Algorithms are evaluated using an advanced, system-level simulator of an M-MIMO Heterogeneous Network (HetNet) utilizing an accurate 3D-ray-tracing radio channel model. The proposed RL-based BSs switching algorithm is proven to provide 70% gains in EE over a state-of-the-art algorithm using an analytical heuristic. Moreover, the proposed action filtering and REM-EA can reduce RL convergence time in relation to the best-performing state-of-the-art exploration method by 60% and 83%, respectively.