Deep Reinforcement Learning for Dynamic Power Allocation in Cell-free mmWave Massive MIMO

Yu Zhao, Ignas Niemegeers, Sonia Heemstra de Groot

2021

Abstract

Numerical optimization has been investigated for decades to solve complex problems in wireless communication systems. This has resulted in many effective methods, e.g., the weighted minimum mean square error (WMMSE) algorithm. However, these methods often incur a high computational cost, making their application to time-constrained problems difficult. Recently data-driven methods have attracted a lot of attention due to their near-optimal performance with affordable computational cost. Deep reinforcement learning (DRL) is one of the most promising optimization methods for future wireless communication systems. In this paper, we investigate the DRL method, using a deep Q-network (DQN), to allocate the downlink transmission power in cell-free (CF) mmWave massive multiple-input multiple-output (MIMO) systems. We consider the sum spectral efficiency (SE) optimization for systems with mobile user equipment (UEs). The DQN is trained by the rewards of trial-and-error interactions with the environment over time. It takes as input the long-term fading information and it outputs the downlink transmission power values. The numerical results, obtained for a particular 3GPP scenario, show that DQN outperforms WMMSE in terms of sum-SE and has a much lower computational complexity.

Paper Citation

in Harvard Style

Zhao Y., Niemegeers I. and Heemstra de Groot S. (2021). Deep Reinforcement Learning for Dynamic Power Allocation in Cell-free mmWave Massive MIMO. In Proceedings of the 18th International Conference on Wireless Networks and Mobile Systems - Volume 1: WINSYS, ISBN 978-989-758-529-6, pages 33-45. DOI: 10.5220/0010617300330045

in Bibtex Style

@conference{winsys21,
author={Yu Zhao and Ignas Niemegeers and Sonia Heemstra de Groot},
title={Deep Reinforcement Learning for Dynamic Power Allocation in Cell-free mmWave Massive MIMO},
booktitle={Proceedings of the 18th International Conference on Wireless Networks and Mobile Systems - Volume 1: WINSYS,},
year={2021},
pages={33-45},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010617300330045},
isbn={978-989-758-529-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Conference on Wireless Networks and Mobile Systems - Volume 1: WINSYS,
TI - Deep Reinforcement Learning for Dynamic Power Allocation in Cell-free mmWave Massive MIMO
SN - 978-989-758-529-6
AU - Zhao Y.
AU - Niemegeers I.
AU - Heemstra de Groot S.
PY - 2021
SP - 33
EP - 45
DO - 10.5220/0010617300330045