Multi-panel extra-large scale MIMO based joint activity detection and channel estimation for near-field massive IoT access

Zhen Gao; Hanlin Xiu; Yikun Mei; Anwen Liao; Malong Ke; Chun Hu; Mohamed Slim Alouini

doi:10.23919/JCC.fa.2022-0138.202305

Multi-panel extra-large scale MIMO based joint activity detection and channel estimation for near-field massive IoT access

Zhen Gao, Hanlin Xiu, Yikun Mei, Anwen Liao, Malong Ke, Chun Hu^*, Mohamed Slim Alouini

^*Corresponding author for this work

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

4 Scopus citations

Abstract

The extra-large scale multiple-input multiple-output (XL-MIMO) for the beyond fifth/sixth generation mobile communications is a promising technology to provide Tbps data transmission and stable access service. However, the extremely large antenna array aperture arouses the channel near-field effect, resulting in the deteriorated data rate and other challenges in the practice communication systems. Meanwhile, multi-panel MIMO technology has attracted extensive attention due to its flexible configuration, low hardware cost, and wider coverage. By combining the XL-MIMO and multi-panel array structure, we construct multi-panel XL-MIMO and apply it to massive Internet of Things (IoT) access. First, we model the multi-panel XL-MIMO-based near-field channels for massive IoT access scenarios, where the electromagnetic waves corresponding to different panels have different angles of arrival/departure (AoAs/AoDs). Then, by exploiting the sparsity of the near-field massive IoT access channels, we formulate a compressed sensing based joint active user detection (AUD) and channel estimation (CE) problem which is solved by AMP-EM-MMV algorithm. The simulation results exhibit the superiority of the AMP-EM-MMV based joint AUD and CE scheme over the baseline algorithms.

Original language	English (US)
Pages (from-to)	232-243
Number of pages	12
Journal	China Communications
Volume	20
Issue number	5
DOIs	https://doi.org/10.23919/JCC.fa.2022-0138.202305
State	Published - May 1 2023

Bibliographical note

Funding Information:
This work was supported by National Key Research and Development Program of China under Grants 2021YFB1600500, 2021YFB3201502, and 2022YFB3207704, Natural Science Foundation of China (NSFC) under Grants U2233216, 62071044, 61827901, 62088101 and 62201056, supported by Shandong Province Natural Science Foundation under Grant ZR2022YQ62, and supported by Beijing Nova Program, Beijing Institute of Technology Research Fund Program for Young Scholars under grant XSQD-202121009

Publisher Copyright:
© 2013 China Institute of Communications.

Keywords

active user detection
approximate message passing
channel estimation
extra-large scale MIMO
massive IoT access
multipanel

ASJC Scopus subject areas

Computer Networks and Communications
Electrical and Electronic Engineering

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10.23919/JCC.fa.2022-0138.202305

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title = "Multi-panel extra-large scale MIMO based joint activity detection and channel estimation for near-field massive IoT access",

abstract = "The extra-large scale multiple-input multiple-output (XL-MIMO) for the beyond fifth/sixth generation mobile communications is a promising technology to provide Tbps data transmission and stable access service. However, the extremely large antenna array aperture arouses the channel near-field effect, resulting in the deteriorated data rate and other challenges in the practice communication systems. Meanwhile, multi-panel MIMO technology has attracted extensive attention due to its flexible configuration, low hardware cost, and wider coverage. By combining the XL-MIMO and multi-panel array structure, we construct multi-panel XL-MIMO and apply it to massive Internet of Things (IoT) access. First, we model the multi-panel XL-MIMO-based near-field channels for massive IoT access scenarios, where the electromagnetic waves corresponding to different panels have different angles of arrival/departure (AoAs/AoDs). Then, by exploiting the sparsity of the near-field massive IoT access channels, we formulate a compressed sensing based joint active user detection (AUD) and channel estimation (CE) problem which is solved by AMP-EM-MMV algorithm. The simulation results exhibit the superiority of the AMP-EM-MMV based joint AUD and CE scheme over the baseline algorithms.",

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author = "Zhen Gao and Hanlin Xiu and Yikun Mei and Anwen Liao and Malong Ke and Chun Hu and Alouini, {Mohamed Slim}",

note = "Funding Information: This work was supported by National Key Research and Development Program of China under Grants 2021YFB1600500, 2021YFB3201502, and 2022YFB3207704, Natural Science Foundation of China (NSFC) under Grants U2233216, 62071044, 61827901, 62088101 and 62201056, supported by Shandong Province Natural Science Foundation under Grant ZR2022YQ62, and supported by Beijing Nova Program, Beijing Institute of Technology Research Fund Program for Young Scholars under grant XSQD-202121009 Publisher Copyright: {\textcopyright} 2013 China Institute of Communications.",

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AU - Xiu, Hanlin

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AU - Liao, Anwen

AU - Ke, Malong

AU - Hu, Chun

AU - Alouini, Mohamed Slim

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PY - 2023/5/1

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N2 - The extra-large scale multiple-input multiple-output (XL-MIMO) for the beyond fifth/sixth generation mobile communications is a promising technology to provide Tbps data transmission and stable access service. However, the extremely large antenna array aperture arouses the channel near-field effect, resulting in the deteriorated data rate and other challenges in the practice communication systems. Meanwhile, multi-panel MIMO technology has attracted extensive attention due to its flexible configuration, low hardware cost, and wider coverage. By combining the XL-MIMO and multi-panel array structure, we construct multi-panel XL-MIMO and apply it to massive Internet of Things (IoT) access. First, we model the multi-panel XL-MIMO-based near-field channels for massive IoT access scenarios, where the electromagnetic waves corresponding to different panels have different angles of arrival/departure (AoAs/AoDs). Then, by exploiting the sparsity of the near-field massive IoT access channels, we formulate a compressed sensing based joint active user detection (AUD) and channel estimation (CE) problem which is solved by AMP-EM-MMV algorithm. The simulation results exhibit the superiority of the AMP-EM-MMV based joint AUD and CE scheme over the baseline algorithms.

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Multi-panel extra-large scale MIMO based joint activity detection and channel estimation for near-field massive IoT access

Abstract

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Keywords

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