Joint activity detection and channel estimation for massive IoT access based on Millimeter-Wave/Terahertz Multi-Panel Massive MIMO
Joint activity detection and channel estimation for massive IoT access based on Millimeter-Wave/Terahertz Multi-Panel Massive MIMO
The multi-panel array, as a state-of-the-art antennain-package technology, is very suitable for millimeter-wave (mmWave)/terahertz (THz) systems, due to its low-cost deployment and scalable configuration. But in the context of nonuniform array structures it leads to intractable signal processing. Based on such an array structure at the base station, this paper investigates a joint active user detection (AUD) and channel estimation (CE) scheme based on compressive sensing (CS) for application to the massive Internet of Things (IoT). Specifically, by exploiting the structured sparsity of mmWave/THz massive IoT access channels, we firstly formulate the multi-panel massive multiple-input multiple-output (mMIMO)-based joint AUD and CE problem as a multiple measurement vector (MMV)-CS problem. Then, we harness the expectation maximization (EM) algorithm to learn the prior parameters (i.e., the noise variance and the sparsity ratio) and an orthogonal approximate message passing (OAMP)-EM-MMV algorithm is developed to solve this problem. Our simulation results verify the improved AUD and CE performance of the proposed scheme compared to conventional CS-based algorithms.
Antenna arrays, Internet of Things, Massive IoT access, Millimeter wave communication, Millimeter wave technology, Radio frequency, Signal processing algorithms, Uplink, active user detection, channel estimation, millimeter-wave, multi-panel mMIMO, terahertz
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Xiu, Hanlin
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Gao, Zhen
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Liao, Anwen
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Mei, Yikun
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Tan, Shufeng
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Zheng, Dezhi
da0121e5-27b5-4e40-ac8b-ec8b10160fce
Renzo, Marco Di
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Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Xiu, Hanlin
5806a23a-3e65-442f-a742-854b470aa878
Gao, Zhen
e0ab17e4-5297-4334-8b64-87924feb7876
Liao, Anwen
83b2105c-a8a3-4ccc-bf9d-22862a756ebb
Mei, Yikun
a6724b4c-a756-41cf-9052-6bc8f8df94f6
Tan, Shufeng
32a0e967-7517-4d7e-b44c-ca9f1c7c5d4c
Zheng, Dezhi
da0121e5-27b5-4e40-ac8b-ec8b10160fce
Renzo, Marco Di
851ec05a-0f5d-49b1-aaf6-563604f8b809
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Xiu, Hanlin, Gao, Zhen, Liao, Anwen, Mei, Yikun, Tan, Shufeng, Zheng, Dezhi, Renzo, Marco Di and Hanzo, Lajos
(2022)
Joint activity detection and channel estimation for massive IoT access based on Millimeter-Wave/Terahertz Multi-Panel Massive MIMO.
IEEE Transactions on Vehicular Technology, .
(doi:10.1109/TVT.2022.3206492).
Abstract
The multi-panel array, as a state-of-the-art antennain-package technology, is very suitable for millimeter-wave (mmWave)/terahertz (THz) systems, due to its low-cost deployment and scalable configuration. But in the context of nonuniform array structures it leads to intractable signal processing. Based on such an array structure at the base station, this paper investigates a joint active user detection (AUD) and channel estimation (CE) scheme based on compressive sensing (CS) for application to the massive Internet of Things (IoT). Specifically, by exploiting the structured sparsity of mmWave/THz massive IoT access channels, we firstly formulate the multi-panel massive multiple-input multiple-output (mMIMO)-based joint AUD and CE problem as a multiple measurement vector (MMV)-CS problem. Then, we harness the expectation maximization (EM) algorithm to learn the prior parameters (i.e., the noise variance and the sparsity ratio) and an orthogonal approximate message passing (OAMP)-EM-MMV algorithm is developed to solve this problem. Our simulation results verify the improved AUD and CE performance of the proposed scheme compared to conventional CS-based algorithms.
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Joint Activity Detection and Channel Estimation for Massive IoT Access Based on Millimeter-WaveTerahertz Multi-Panel Massive MIMO
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Joint_Activity_Detection_and_Channel_Estimation_for_Massive_IoT_Access_Based_on_Millimeter-Wave_Terahertz_Multi-Panel_Massive_MIMO
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Accepted/In Press date: 9 September 2022
e-pub ahead of print date: 14 September 2022
Additional Information:
Funding: The work of Z. Gao is supported by Natural Science Foundation of China (NSFC) under Grant 62071044. L. Hanzo would like to acknowledge the financial support of the Engineering and Physical Sciences Research Council projects EP/W016605/1 and EP/P003990/1 (COALESCE) as well as of the European Research Council’s Advanced Fellow Grant QuantCom (Grant No. 789028)
Keywords:
Antenna arrays, Internet of Things, Massive IoT access, Millimeter wave communication, Millimeter wave technology, Radio frequency, Signal processing algorithms, Uplink, active user detection, channel estimation, millimeter-wave, multi-panel mMIMO, terahertz
Identifiers
Local EPrints ID: 470494
URI: http://eprints.soton.ac.uk/id/eprint/470494
ISSN: 0018-9545
PURE UUID: 49ada1d2-ed54-4198-96e0-0930ce642d1c
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Date deposited: 11 Oct 2022 17:00
Last modified: 18 Mar 2024 02:36
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Contributors
Author:
Hanlin Xiu
Author:
Zhen Gao
Author:
Anwen Liao
Author:
Yikun Mei
Author:
Shufeng Tan
Author:
Dezhi Zheng
Author:
Marco Di Renzo
Author:
Lajos Hanzo
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