loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Yu Zhao ; Ignas Niemegeers and Sonia Heemstra de Groot

Affiliation: Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands

Keyword(s): Cell-free Massive MIMO, Deep Reinforcement Learning, Deep Q-Network, Power Allocation.

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 e nvironment 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. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 35.170.54.171

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
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 - WINSYS; ISBN 978-989-758-529-6; ISSN 2184-948X, SciTePress, pages 33-45. DOI: 10.5220/0010617300330045

@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 - WINSYS},
year={2021},
pages={33-45},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010617300330045},
isbn={978-989-758-529-6},
issn={2184-948X},
}

TY - CONF

JO - Proceedings of the 18th International Conference on Wireless Networks and Mobile Systems - WINSYS
TI - Deep Reinforcement Learning for Dynamic Power Allocation in Cell-free mmWave Massive MIMO
SN - 978-989-758-529-6
IS - 2184-948X
AU - Zhao, Y.
AU - Niemegeers, I.
AU - Heemstra de Groot, S.
PY - 2021
SP - 33
EP - 45
DO - 10.5220/0010617300330045
PB - SciTePress