Bayesian learning-based doubly-selective sparse channel estimation for millimeter wave hybrid MIMO-FBMC-OQAM systems
Bayesian learning-based doubly-selective sparse channel estimation for millimeter wave hybrid MIMO-FBMC-OQAM systems
We design and analyse filter bank multicarrier (FBMC) offset quadrature amplitude modulation (OQAM)-based millimeter wave (mmWave) hybrid multiple-input multiple-output (MIMO) systems. Furthermore, a novel channel estimation model is conceived for quasi-static mmWave hybrid MIMO-FBMC-OQAM (mmH-MFO) systems that reconfigures the radio-frequency (RF) circuitry during the transmission of zero symbols. Subsequently, a Bayesian learning (BL) technique is proposed for sparse channel estimation, which relies on multiple measurement vectors combined with selective subcarrier grouping for enhanced estimation. Additionally, an online BL based Kalman filter (OBL-KF) is designed for sparse channel tracking in doubly-selective mmH-MFO systems. Then the Bayesian Cramer-Rao lower bounds (BCRLBs) are derived for characterizing the performance of the proposed frequency-selective and doubly-selective channel estimation techniques. Finally, a limited feedback based algorithm relying on beamspace channel estimates is proposed for hybrid precoder/combiner design. The accuracy of our analytical results is confirmed by our simulation results.
Bayesian Cramer-Rao bound, Bayesian learning (BL), expectation maximization, filter bank multicarrier, hybrid MIMO architecture, mmWave communication
529-543
Srivastava, Suraj
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Singh, Prem
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Jagannatham, Aditya K.
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Karandikar, Abhay
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Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
January 2021
Srivastava, Suraj
7b40cb6c-7bc6-402c-8751-24346d39002c
Singh, Prem
b3155131-3d31-4d82-876c-33655420c7e5
Jagannatham, Aditya K.
ea2f628b-0f2a-48a3-a293-122c809757aa
Karandikar, Abhay
10d741d5-4798-4cf1-92f8-f675c583abf5
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Srivastava, Suraj, Singh, Prem, Jagannatham, Aditya K., Karandikar, Abhay and Hanzo, Lajos
(2021)
Bayesian learning-based doubly-selective sparse channel estimation for millimeter wave hybrid MIMO-FBMC-OQAM systems.
IEEE Transactions on Communications, 69 (1), , [9217576].
(doi:10.1109/TCOMM.2020.3029568).
Abstract
We design and analyse filter bank multicarrier (FBMC) offset quadrature amplitude modulation (OQAM)-based millimeter wave (mmWave) hybrid multiple-input multiple-output (MIMO) systems. Furthermore, a novel channel estimation model is conceived for quasi-static mmWave hybrid MIMO-FBMC-OQAM (mmH-MFO) systems that reconfigures the radio-frequency (RF) circuitry during the transmission of zero symbols. Subsequently, a Bayesian learning (BL) technique is proposed for sparse channel estimation, which relies on multiple measurement vectors combined with selective subcarrier grouping for enhanced estimation. Additionally, an online BL based Kalman filter (OBL-KF) is designed for sparse channel tracking in doubly-selective mmH-MFO systems. Then the Bayesian Cramer-Rao lower bounds (BCRLBs) are derived for characterizing the performance of the proposed frequency-selective and doubly-selective channel estimation techniques. Finally, a limited feedback based algorithm relying on beamspace channel estimates is proposed for hybrid precoder/combiner design. The accuracy of our analytical results is confirmed by our simulation results.
Text
mmWAVE_MFBMC_paper
- Accepted Manuscript
More information
Accepted/In Press date: 30 September 2020
Published date: January 2021
Additional Information:
Funding Information:
Manuscript received June 14, 2020; revised September 25, 2020; accepted September 30, 2020. Date of publication October 8, 2020; date of current version January 15, 2021. L. Hanzo would like to acknowledge the financial support of the Engineering and Physical Sciences Research Council projects EP/N004558/1, EP/P034284/1, EP/P034284/1, EP/P003990/1 (COALESCE), of the Royal Society’s Global Challenges Research Fund Grant as well as of the European Research Council’s Advanced Fellow Grant QuantCom. A. K. Jagannatham would like to acknowledge the research supported by the Science and Engineering Research Board (SERB), Department of Science and Technology, Government of India, Space Technology Cell, IIT Kanpur, IIMA IDEA Telecom Centre of Excellence, Qualcomm Innovation Fellowship and Arun Kumar Chair Professorship. The associate editor coordinating the review of this article and approving it for publication was L. Cottatellucci. (Corresponding author: Lajos Hanzo.) Suraj Srivastava, Prem Singh, and Aditya K. Jagannatham are with the Department of Electrical Engineering, Indian Institute of Technology Kan-pur, Kanpur 208016, India (e-mail: ssrivast@iitk.ac.in; psrawat@iitk.ac.in; adityaj@iitk.ac.in).
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© 1972-2012 IEEE.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
Keywords:
Bayesian Cramer-Rao bound, Bayesian learning (BL), expectation maximization, filter bank multicarrier, hybrid MIMO architecture, mmWave communication
Identifiers
Local EPrints ID: 444338
URI: http://eprints.soton.ac.uk/id/eprint/444338
ISSN: 0090-6778
PURE UUID: 55a5c1f6-016a-4bb1-adc2-f11337f873bc
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Date deposited: 13 Oct 2020 16:47
Last modified: 18 Mar 2024 05:26
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Author:
Suraj Srivastava
Author:
Prem Singh
Author:
Aditya K. Jagannatham
Author:
Abhay Karandikar
Author:
Lajos Hanzo
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