Capturing the Sparsity for Massive MIMO Channel with Approximate Message Passing

Xudong Han; Shun Zhang; Anteneh Mohammed; Weile Zhang; Nan Zhao; Yuantao Gu

doi:10.1007/978-981-13-9409-6_26

Capturing the Sparsity for Massive MIMO Channel with Approximate Message Passing

Xudong Han
, Shun Zhang
, Anteneh Mohammed
, Weile Zhang
, Nan Zhao
, Yuantao Gu

Faculty of Electronic and Information Engineering

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

Abstract

In this work, we propose a low-overhead characteristic learning mechanism for the time-varying massive MIMO channels. Specially, we exploit the common sparsity and temporal correlation of the channel. Firstly, using VCR and modeling the temporal correlation as an autoregressive process, we formulate the dynamic massive MIMO channel as a sparse signal model. Then, an expectation maximization (EM) based sparse Bayesian learning (SBL) framework is developed to learn model parameters. To achieve the posteriors of model parameters, approximate message passing (AMP) is utilized in the expectation step. Finally, we demonstrate the performance through numerical simulations.

Original language	English
Title of host publication	Communications, Signal Processing, and Systems - Proceedings of the 8th International Conference on Communications, Signal Processing, and Systems, CSPS 2019
Editors	Qilian Liang, Wei Wang, Xin Liu, Zhenyu Na, Min Jia, Baoju Zhang
Publisher	Springer
Pages	214-222
Number of pages	9
ISBN (Print)	9789811394089
DOIs	https://doi.org/10.1007/978-981-13-9409-6_26
State	Published - 2020
Event	8th International Conference on Communications, Signal Processing, and Systems, CSPS 2019 - Urumqi, China Duration: 20 Jul 2019 → 22 Jul 2019

Publication series

Name	Lecture Notes in Electrical Engineering
Volume	571 LNEE
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Conference

Conference	8th International Conference on Communications, Signal Processing, and Systems, CSPS 2019
Country/Territory	China
City	Urumqi
Period	20/07/19 → 22/07/19

Keywords

Approximate message passing
Expectation maximization
Massive MIMO
Sparse Bayesian learning

Access to Document

10.1007/978-981-13-9409-6_26

Cite this

Han, X., Zhang, S., Mohammed, A., Zhang, W., Zhao, N., & Gu, Y. (2020). Capturing the Sparsity for Massive MIMO Channel with Approximate Message Passing. In Q. Liang, W. Wang, X. Liu, Z. Na, M. Jia, & B. Zhang (Eds.), Communications, Signal Processing, and Systems - Proceedings of the 8th International Conference on Communications, Signal Processing, and Systems, CSPS 2019 (pp. 214-222). (Lecture Notes in Electrical Engineering; Vol. 571 LNEE). Springer. https://doi.org/10.1007/978-981-13-9409-6_26

Han, Xudong ; Zhang, Shun ; Mohammed, Anteneh et al. / Capturing the Sparsity for Massive MIMO Channel with Approximate Message Passing. Communications, Signal Processing, and Systems - Proceedings of the 8th International Conference on Communications, Signal Processing, and Systems, CSPS 2019. editor / Qilian Liang ; Wei Wang ; Xin Liu ; Zhenyu Na ; Min Jia ; Baoju Zhang. Springer, 2020. pp. 214-222 (Lecture Notes in Electrical Engineering).

@inproceedings{de2a85ecca5442bf90af665c6df3dfe4,

title = "Capturing the Sparsity for Massive MIMO Channel with Approximate Message Passing",

abstract = "In this work, we propose a low-overhead characteristic learning mechanism for the time-varying massive MIMO channels. Specially, we exploit the common sparsity and temporal correlation of the channel. Firstly, using VCR and modeling the temporal correlation as an autoregressive process, we formulate the dynamic massive MIMO channel as a sparse signal model. Then, an expectation maximization (EM) based sparse Bayesian learning (SBL) framework is developed to learn model parameters. To achieve the posteriors of model parameters, approximate message passing (AMP) is utilized in the expectation step. Finally, we demonstrate the performance through numerical simulations.",

keywords = "Approximate message passing, Expectation maximization, Massive MIMO, Sparse Bayesian learning",

author = "Xudong Han and Shun Zhang and Anteneh Mohammed and Weile Zhang and Nan Zhao and Yuantao Gu",

note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Singapore Pte Ltd.; 8th International Conference on Communications, Signal Processing, and Systems, CSPS 2019 ; Conference date: 20-07-2019 Through 22-07-2019",

year = "2020",

doi = "10.1007/978-981-13-9409-6\_26",

language = "英语",

isbn = "9789811394089",

series = "Lecture Notes in Electrical Engineering",

publisher = "Springer",

pages = "214--222",

editor = "Qilian Liang and Wei Wang and Xin Liu and Zhenyu Na and Min Jia and Baoju Zhang",

booktitle = "Communications, Signal Processing, and Systems - Proceedings of the 8th International Conference on Communications, Signal Processing, and Systems, CSPS 2019",

}

Han, X, Zhang, S, Mohammed, A, Zhang, W, Zhao, N & Gu, Y 2020, Capturing the Sparsity for Massive MIMO Channel with Approximate Message Passing. in Q Liang, W Wang, X Liu, Z Na, M Jia & B Zhang (eds), Communications, Signal Processing, and Systems - Proceedings of the 8th International Conference on Communications, Signal Processing, and Systems, CSPS 2019. Lecture Notes in Electrical Engineering, vol. 571 LNEE, Springer, pp. 214-222, 8th International Conference on Communications, Signal Processing, and Systems, CSPS 2019, Urumqi, China, 20/07/19. https://doi.org/10.1007/978-981-13-9409-6_26

Capturing the Sparsity for Massive MIMO Channel with Approximate Message Passing. / Han, Xudong; Zhang, Shun; Mohammed, Anteneh et al.
Communications, Signal Processing, and Systems - Proceedings of the 8th International Conference on Communications, Signal Processing, and Systems, CSPS 2019. ed. / Qilian Liang; Wei Wang; Xin Liu; Zhenyu Na; Min Jia; Baoju Zhang. Springer, 2020. p. 214-222 (Lecture Notes in Electrical Engineering; Vol. 571 LNEE).

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

TY - GEN

T1 - Capturing the Sparsity for Massive MIMO Channel with Approximate Message Passing

AU - Han, Xudong

AU - Zhang, Shun

AU - Mohammed, Anteneh

AU - Zhang, Weile

AU - Zhao, Nan

AU - Gu, Yuantao

PY - 2020

Y1 - 2020

N2 - In this work, we propose a low-overhead characteristic learning mechanism for the time-varying massive MIMO channels. Specially, we exploit the common sparsity and temporal correlation of the channel. Firstly, using VCR and modeling the temporal correlation as an autoregressive process, we formulate the dynamic massive MIMO channel as a sparse signal model. Then, an expectation maximization (EM) based sparse Bayesian learning (SBL) framework is developed to learn model parameters. To achieve the posteriors of model parameters, approximate message passing (AMP) is utilized in the expectation step. Finally, we demonstrate the performance through numerical simulations.

AB - In this work, we propose a low-overhead characteristic learning mechanism for the time-varying massive MIMO channels. Specially, we exploit the common sparsity and temporal correlation of the channel. Firstly, using VCR and modeling the temporal correlation as an autoregressive process, we formulate the dynamic massive MIMO channel as a sparse signal model. Then, an expectation maximization (EM) based sparse Bayesian learning (SBL) framework is developed to learn model parameters. To achieve the posteriors of model parameters, approximate message passing (AMP) is utilized in the expectation step. Finally, we demonstrate the performance through numerical simulations.

KW - Approximate message passing

KW - Expectation maximization

KW - Massive MIMO

KW - Sparse Bayesian learning

UR - https://www.scopus.com/pages/publications/85084737548

U2 - 10.1007/978-981-13-9409-6_26

DO - 10.1007/978-981-13-9409-6_26

M3 - 会议稿件

AN - SCOPUS:85084737548

SN - 9789811394089

T3 - Lecture Notes in Electrical Engineering

SP - 214

EP - 222

BT - Communications, Signal Processing, and Systems - Proceedings of the 8th International Conference on Communications, Signal Processing, and Systems, CSPS 2019

A2 - Liang, Qilian

A2 - Wang, Wei

A2 - Liu, Xin

A2 - Na, Zhenyu

A2 - Jia, Min

A2 - Zhang, Baoju

PB - Springer

T2 - 8th International Conference on Communications, Signal Processing, and Systems, CSPS 2019

Y2 - 20 July 2019 through 22 July 2019

ER -

Han X, Zhang S, Mohammed A, Zhang W, Zhao N, Gu Y. Capturing the Sparsity for Massive MIMO Channel with Approximate Message Passing. In Liang Q, Wang W, Liu X, Na Z, Jia M, Zhang B, editors, Communications, Signal Processing, and Systems - Proceedings of the 8th International Conference on Communications, Signal Processing, and Systems, CSPS 2019. Springer. 2020. p. 214-222. (Lecture Notes in Electrical Engineering). doi: 10.1007/978-981-13-9409-6_26

Capturing the Sparsity for Massive MIMO Channel with Approximate Message Passing

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Keywords

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