Deep scanning—beam selection based on deep reinforcement learning in massive mimo wireless communication system

Minhoe Kim; Woongsup Lee; Dong Ho Cho

doi:10.3390/electronics9111844

Deep scanning—beam selection based on deep reinforcement learning in massive mimo wireless communication system

Minhoe Kim, Woongsup Lee, Dong Ho Cho

Graduate School of Information

Research output: Contribution to journal › Article › peer-review

Abstract

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 Scanning, in which a near-optimal beamforming vector can be found based on deep Q-learning. Through simulations, we confirm that the optimal beam vector can be found with a high probability. We also show that the complexity required to find the optimum beam vector can be reduced significantly in comparison with conventional beam search schemes.

Original language	English
Article number	1844
Pages (from-to)	1-10
Number of pages	10
Journal	Electronics (Switzerland)
Volume	9
Issue number	11
DOIs	https://doi.org/10.3390/electronics9111844
Publication status	Published - 2020 Nov

Bibliographical note

Funding Information:
Funding: This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2018R1D1A1B07040796).

Publisher Copyright:
© 2020 by the authors. Licensee MDPI, Basel, Switzerland.

All Science Journal Classification (ASJC) codes

Control and Systems Engineering
Signal Processing
Hardware and Architecture
Computer Networks and Communications
Electrical and Electronic Engineering

Access to Document

10.3390/electronics9111844

Cite this

@article{f44d9133bc824d8c9bf6732eae95b797,

title = "Deep scanning—beam selection based on deep reinforcement learning in massive mimo wireless communication system",

abstract = "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 Scanning, in which a near-optimal beamforming vector can be found based on deep Q-learning. Through simulations, we confirm that the optimal beam vector can be found with a high probability. We also show that the complexity required to find the optimum beam vector can be reduced significantly in comparison with conventional beam search schemes.",

author = "Minhoe Kim and Woongsup Lee and Cho, {Dong Ho}",

note = "Funding Information: Funding: This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2018R1D1A1B07040796). Publisher Copyright: {\textcopyright} 2020 by the authors. Licensee MDPI, Basel, Switzerland.",

year = "2020",

month = nov,

doi = "10.3390/electronics9111844",

language = "English",

volume = "9",

pages = "1--10",

journal = "Electronics (Switzerland)",

issn = "2079-9292",

publisher = "Multidisciplinary Digital Publishing Institute (MDPI)",

number = "11",

}

TY - JOUR

T1 - Deep scanning—beam selection based on deep reinforcement learning in massive mimo wireless communication system

AU - Kim, Minhoe

AU - Lee, Woongsup

AU - Cho, Dong Ho

N1 - Funding Information: Funding: This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2018R1D1A1B07040796). Publisher Copyright: © 2020 by the authors. Licensee MDPI, Basel, Switzerland.

PY - 2020/11

Y1 - 2020/11

N2 - 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 Scanning, in which a near-optimal beamforming vector can be found based on deep Q-learning. Through simulations, we confirm that the optimal beam vector can be found with a high probability. We also show that the complexity required to find the optimum beam vector can be reduced significantly in comparison with conventional beam search schemes.

AB - 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 Scanning, in which a near-optimal beamforming vector can be found based on deep Q-learning. Through simulations, we confirm that the optimal beam vector can be found with a high probability. We also show that the complexity required to find the optimum beam vector can be reduced significantly in comparison with conventional beam search schemes.

UR - http://www.scopus.com/inward/record.url?scp=85095747312&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85095747312&partnerID=8YFLogxK

U2 - 10.3390/electronics9111844

DO - 10.3390/electronics9111844

M3 - Article

AN - SCOPUS:85095747312

SN - 2079-9292

VL - 9

SP - 1

EP - 10

JO - Electronics (Switzerland)

JF - Electronics (Switzerland)

IS - 11

M1 - 1844

ER -

Deep scanning—beam selection based on deep reinforcement learning in massive mimo wireless communication system

Abstract

Bibliographical note

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this