Expectation propagation-based active user detection and channel estimation for massive machine-type communications

Jinyoup Ahn; Byonghyo Shim; Kwang Bok Lee

doi:10.1109/ICCW.2018.8403604

Expectation propagation-based active user detection and channel estimation for massive machine-type communications

Jinyoup Ahn, Byonghyo Shim, Kwang Bok Lee

Department of Computer Science and Engineering

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

2 Citations (Scopus)

Abstract

In massive machine-type communication (mMTC), by utilizing sporadic device activities, compressed sensing based multi-user detection (CS-MUD) can be used to recover sparse multi-user vectors in the grant-free uplink non-orthogonal multiple access (NOMA) environments. In CS-MUD, the channel state information (CSI) between each active device and the basestation should be estimated before the symbol detection. In this paper, we propose a novel Bayesian joint active user detection (AUD) and channel estimation (CE) method based on the expectation propagation (EP) algorithm. The proposed method finds the best Gaussian approximation for the computationally intractable posterior distribution of the sparse channel vector using iterative EP parameter update rules. Using the approximated distribution, identification and CSI estimation of active devices are jointly performed. We show from numerical simulations that the proposed technique greatly improves the performance of AUD and CE.

Original language	English
Title of host publication	2018 IEEE International Conference on Communications Workshops, ICC Workshops 2018 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1-6
Number of pages	6
ISBN (Electronic)	9781538643280
DOIs	https://doi.org/10.1109/ICCW.2018.8403604
Publication status	Published - 2018 Jul 3
Event	2018 IEEE International Conference on Communications Workshops, ICC Workshops 2018 - Kansas City, United States Duration: 2018 May 20 → 2018 May 24

Publication series

Name	2018 IEEE International Conference on Communications Workshops, ICC Workshops 2018 - Proceedings

Other

Other	2018 IEEE International Conference on Communications Workshops, ICC Workshops 2018
Country/Territory	United States
City	Kansas City
Period	18/5/20 → 18/5/24

Bibliographical note

Funding Information:
ACKNOWLEDGMENT This work was supported by the Institute for Information and Communications Technology Promotion through Korea Government under grant 2016-0-00209, and LG Electronics Co. Ltd.

Publisher Copyright:
© 2018 IEEE.

Keywords

Active user detection
Channel estimation
Compressed sensing
Expectation propagation
Massive machine-type communication
Nonorthogonal multiple access

ASJC Scopus subject areas

Computer Networks and Communications
Computer Science Applications
Hardware and Architecture

Access to Document

10.1109/ICCW.2018.8403604

Cite this

Ahn, J., Shim, B., & Lee, K. B. (2018). Expectation propagation-based active user detection and channel estimation for massive machine-type communications. In 2018 IEEE International Conference on Communications Workshops, ICC Workshops 2018 - Proceedings (pp. 1-6). (2018 IEEE International Conference on Communications Workshops, ICC Workshops 2018 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCW.2018.8403604

Expectation propagation-based active user detection and channel estimation for massive machine-type communications. / Ahn, Jinyoup; Shim, Byonghyo; Lee, Kwang Bok.
2018 IEEE International Conference on Communications Workshops, ICC Workshops 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. p. 1-6 (2018 IEEE International Conference on Communications Workshops, ICC Workshops 2018 - Proceedings).

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

Ahn, J, Shim, B & Lee, KB 2018, Expectation propagation-based active user detection and channel estimation for massive machine-type communications. in 2018 IEEE International Conference on Communications Workshops, ICC Workshops 2018 - Proceedings. 2018 IEEE International Conference on Communications Workshops, ICC Workshops 2018 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 1-6, 2018 IEEE International Conference on Communications Workshops, ICC Workshops 2018, Kansas City, United States, 18/5/20. https://doi.org/10.1109/ICCW.2018.8403604

Ahn J, Shim B, Lee KB. Expectation propagation-based active user detection and channel estimation for massive machine-type communications. In 2018 IEEE International Conference on Communications Workshops, ICC Workshops 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2018. p. 1-6. (2018 IEEE International Conference on Communications Workshops, ICC Workshops 2018 - Proceedings). doi: 10.1109/ICCW.2018.8403604

Ahn, Jinyoup ; Shim, Byonghyo ; Lee, Kwang Bok. / Expectation propagation-based active user detection and channel estimation for massive machine-type communications. 2018 IEEE International Conference on Communications Workshops, ICC Workshops 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 1-6 (2018 IEEE International Conference on Communications Workshops, ICC Workshops 2018 - Proceedings).

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abstract = "In massive machine-type communication (mMTC), by utilizing sporadic device activities, compressed sensing based multi-user detection (CS-MUD) can be used to recover sparse multi-user vectors in the grant-free uplink non-orthogonal multiple access (NOMA) environments. In CS-MUD, the channel state information (CSI) between each active device and the basestation should be estimated before the symbol detection. In this paper, we propose a novel Bayesian joint active user detection (AUD) and channel estimation (CE) method based on the expectation propagation (EP) algorithm. The proposed method finds the best Gaussian approximation for the computationally intractable posterior distribution of the sparse channel vector using iterative EP parameter update rules. Using the approximated distribution, identification and CSI estimation of active devices are jointly performed. We show from numerical simulations that the proposed technique greatly improves the performance of AUD and CE.",

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note = "Funding Information: ACKNOWLEDGMENT This work was supported by the Institute for Information and Communications Technology Promotion through Korea Government under grant 2016-0-00209, and LG Electronics Co. Ltd. Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE International Conference on Communications Workshops, ICC Workshops 2018 ; Conference date: 20-05-2018 Through 24-05-2018",

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N2 - In massive machine-type communication (mMTC), by utilizing sporadic device activities, compressed sensing based multi-user detection (CS-MUD) can be used to recover sparse multi-user vectors in the grant-free uplink non-orthogonal multiple access (NOMA) environments. In CS-MUD, the channel state information (CSI) between each active device and the basestation should be estimated before the symbol detection. In this paper, we propose a novel Bayesian joint active user detection (AUD) and channel estimation (CE) method based on the expectation propagation (EP) algorithm. The proposed method finds the best Gaussian approximation for the computationally intractable posterior distribution of the sparse channel vector using iterative EP parameter update rules. Using the approximated distribution, identification and CSI estimation of active devices are jointly performed. We show from numerical simulations that the proposed technique greatly improves the performance of AUD and CE.

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Expectation propagation-based active user detection and channel estimation for massive machine-type communications

Abstract

Publication series

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Bibliographical note

Keywords

ASJC Scopus subject areas

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