Reinforcement Learning for Energy-Efficient 5G Massive MIMO: Intelligent Antenna Switching

Abstract

To provide users with high throughputs, the fifth generation (5G) and beyond networks are expected to utilize the Massive Multiple-Input Multiple-Output technology (MMIMO), i.e., large antenna arrays. However, additional antennas require the installation of dedicated hardware. As a result the power consumption of a 5G MMIMO network grows. This implies, e.g., higher operator costs. From this angle, the improvement of Energy Efficiency (EE) is identified as one of the key challenges for the 5G and beyond networks. EE can be improved through intelligent antenna switching, i.e., disabling some of the antennas installed at a 5G MMIMO Base Station (BS) when there are few User Equipments (UEs) within the cell area. To improve EE in this scenario we propose to utilize a sub-class of Machine Learning techniques named Reinforcement Learning (RL). Because 5G and beyond networks are expected to come with accurate UE localization, the proposed RL algorithm is based on UE location information stored in an intelligent database named a Radio Environment Map (REM). Two approaches are proposed: first EE is maximized independently for every set of UEs’ positions. After that the process of learning is accelerated by exploiting similarities between data in REM, i.e., REM-Empowered Action Selection Algorithm (REASA) is proposed. The proposed RL algorithms are evaluated with the use of a realistic simulator of the 5G MMIMO network utilizing an accurate 3D-Ray-Tracing radio channel model. The utilization of RL provides about 18.5% EE gains over algorithms based on standard optimization methods. Moreover, when REASA is used the process of learning can be accomplished approximately two times faster.

Keywords

• Reinforcement learning
• massive MIMO
• energy efficiency
• radio environment maps
• 5G
• machine learning