Machine Learning-Based Channel Prediction Exploiting Frequency Correlation in Massive MIMO Wideband Systems

Beomsoo Ko; Hwanjin Kim; Junil Choi

doi:10.1109/ICTC52510.2021.9621119

Machine Learning-Based Channel Prediction Exploiting Frequency Correlation in Massive MIMO Wideband Systems

Beomsoo Ko
, Hwanjin Kim
, Junil Choi

School of Electronics Engineering

Korea Advanced Institute of Science and Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

6 Scopus citations

Abstract

In this paper, we focus on the channel prediction for massive multiple-input multiple-output (MIMO) wideband systems. In massive MIMO systems, the channel prediction is necessary to deal with out-dated channel state information (CSI). A wideband channel is converted into parallel narrowband channels via the orthogonal frequency division multiplexing (OFDM) technique where there exists a frequency correlation among narrowband channels. Thus, we propose a machine learning (ML)-based channel prediction technique, which exploits the frequency correlation. Numerical result shows that, under certain scenarios, the channel predictor trained with a single narrowband channel can support other narrowband channels.

Original language	English
Title of host publication	ICTC 2021 - 12th International Conference on ICT Convergence
Subtitle of host publication	Beyond the Pandemic Era with ICT Convergence Innovation
Publisher	IEEE Computer Society
Pages	1069-1071
Number of pages	3
ISBN (Electronic)	9781665423830
DOIs	https://doi.org/10.1109/ICTC52510.2021.9621119
State	Published - 2021
Event	12th International Conference on Information and Communication Technology Convergence, ICTC 2021 - Jeju Island, Korea, Republic of Duration: 20 Oct 2021 → 22 Oct 2021

Publication series

Name	International Conference on ICT Convergence
Volume	2021-October
ISSN (Print)	2162-1233
ISSN (Electronic)	2162-1241

Conference

Conference	12th International Conference on Information and Communication Technology Convergence, ICTC 2021
Country/Territory	Korea, Republic of
City	Jeju Island
Period	20/10/21 → 22/10/21

Keywords

channel prediction
frequency correlation
machine learning
massive MIMO
wideband

Access to Document

10.1109/ICTC52510.2021.9621119

Cite this

Ko, B., Kim, H., & Choi, J. (2021). Machine Learning-Based Channel Prediction Exploiting Frequency Correlation in Massive MIMO Wideband Systems. In ICTC 2021 - 12th International Conference on ICT Convergence: Beyond the Pandemic Era with ICT Convergence Innovation (pp. 1069-1071). (International Conference on ICT Convergence; Vol. 2021-October). IEEE Computer Society. https://doi.org/10.1109/ICTC52510.2021.9621119

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title = "Machine Learning-Based Channel Prediction Exploiting Frequency Correlation in Massive MIMO Wideband Systems",

abstract = "In this paper, we focus on the channel prediction for massive multiple-input multiple-output (MIMO) wideband systems. In massive MIMO systems, the channel prediction is necessary to deal with out-dated channel state information (CSI). A wideband channel is converted into parallel narrowband channels via the orthogonal frequency division multiplexing (OFDM) technique where there exists a frequency correlation among narrowband channels. Thus, we propose a machine learning (ML)-based channel prediction technique, which exploits the frequency correlation. Numerical result shows that, under certain scenarios, the channel predictor trained with a single narrowband channel can support other narrowband channels.",

keywords = "channel prediction, frequency correlation, machine learning, massive MIMO, wideband",

author = "Beomsoo Ko and Hwanjin Kim and Junil Choi",

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year = "2021",

doi = "10.1109/ICTC52510.2021.9621119",

language = "English",

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Ko, B, Kim, H & Choi, J 2021, Machine Learning-Based Channel Prediction Exploiting Frequency Correlation in Massive MIMO Wideband Systems. in ICTC 2021 - 12th International Conference on ICT Convergence: Beyond the Pandemic Era with ICT Convergence Innovation. International Conference on ICT Convergence, vol. 2021-October, IEEE Computer Society, pp. 1069-1071, 12th International Conference on Information and Communication Technology Convergence, ICTC 2021, Jeju Island, Korea, Republic of, 20/10/21. https://doi.org/10.1109/ICTC52510.2021.9621119

Machine Learning-Based Channel Prediction Exploiting Frequency Correlation in Massive MIMO Wideband Systems. / Ko, Beomsoo; Kim, Hwanjin; Choi, Junil.
ICTC 2021 - 12th International Conference on ICT Convergence: Beyond the Pandemic Era with ICT Convergence Innovation. IEEE Computer Society, 2021. p. 1069-1071 (International Conference on ICT Convergence; Vol. 2021-October).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Machine Learning-Based Channel Prediction Exploiting Frequency Correlation in Massive MIMO Wideband Systems

AU - Ko, Beomsoo

AU - Kim, Hwanjin

AU - Choi, Junil

PY - 2021

Y1 - 2021

N2 - In this paper, we focus on the channel prediction for massive multiple-input multiple-output (MIMO) wideband systems. In massive MIMO systems, the channel prediction is necessary to deal with out-dated channel state information (CSI). A wideband channel is converted into parallel narrowband channels via the orthogonal frequency division multiplexing (OFDM) technique where there exists a frequency correlation among narrowband channels. Thus, we propose a machine learning (ML)-based channel prediction technique, which exploits the frequency correlation. Numerical result shows that, under certain scenarios, the channel predictor trained with a single narrowband channel can support other narrowband channels.

AB - In this paper, we focus on the channel prediction for massive multiple-input multiple-output (MIMO) wideband systems. In massive MIMO systems, the channel prediction is necessary to deal with out-dated channel state information (CSI). A wideband channel is converted into parallel narrowband channels via the orthogonal frequency division multiplexing (OFDM) technique where there exists a frequency correlation among narrowband channels. Thus, we propose a machine learning (ML)-based channel prediction technique, which exploits the frequency correlation. Numerical result shows that, under certain scenarios, the channel predictor trained with a single narrowband channel can support other narrowband channels.

KW - channel prediction

KW - frequency correlation

KW - machine learning

KW - massive MIMO

KW - wideband

UR - https://www.scopus.com/pages/publications/85122933846

U2 - 10.1109/ICTC52510.2021.9621119

DO - 10.1109/ICTC52510.2021.9621119

M3 - Conference contribution

AN - SCOPUS:85122933846

T3 - International Conference on ICT Convergence

SP - 1069

EP - 1071

BT - ICTC 2021 - 12th International Conference on ICT Convergence

PB - IEEE Computer Society

T2 - 12th International Conference on Information and Communication Technology Convergence, ICTC 2021

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Ko B, Kim H, Choi J. Machine Learning-Based Channel Prediction Exploiting Frequency Correlation in Massive MIMO Wideband Systems. In ICTC 2021 - 12th International Conference on ICT Convergence: Beyond the Pandemic Era with ICT Convergence Innovation. IEEE Computer Society. 2021. p. 1069-1071. (International Conference on ICT Convergence). doi: 10.1109/ICTC52510.2021.9621119

Machine Learning-Based Channel Prediction Exploiting Frequency Correlation in Massive MIMO Wideband Systems

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