Deep Scanning—Beam Selection Based on Deep Reinforcement Learning in Massive MIMO Wireless Communication System
In this paper, we investigate a deep learning based resource allocation scheme for massive multiple-input-multiple-output (MIMO) communication systems, where a base station (BS) with a large scale antenna array communicates with a user equipment (UE) using beamforming. In particular, we propose Deep...
Main Authors: | Minhoe Kim, Woongsup Lee, Dong-Ho Cho |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2020-11-01
|
Series: | Electronics |
Subjects: | |
Online Access: | https://www.mdpi.com/2079-9292/9/11/1844 |
Similar Items
-
Deep Reinforcement Learning-Based Coordinated Beamforming for mmWave Massive MIMO Vehicular Networks
by: Pulok Tarafder, et al.
Published: (2023-03-01) -
Deep Reinforcement Learning Based Beam Selection for Hybrid Beamforming and User Grouping in Massive MIMO-NOMA System
by: Irfan Ahmed, et al.
Published: (2022-01-01) -
Deep Reinforcement Learning Based Intelligent User Selection in Massive MIMO Underlay Cognitive Radios
by: Zhaoyuan Shi, et al.
Published: (2019-01-01) -
Dynamic Power Allocation for Cell-Free Massive MIMO: Deep Reinforcement Learning Methods
by: Yu Zhao, et al.
Published: (2021-01-01) -
Suppressing Pilot Contamination in Massive MIMO Downlink via Cross-Frame Scheduling
by: Zezhong Zhang, et al.
Published: (2018-01-01)