Sparse doubly-selective channel estimation techniques for OSTBC MIMO-OFDM systems: a hierarchical Bayesian Kalman filter based approach
Sparse doubly-selective channel estimation techniques for OSTBC MIMO-OFDM systems: a hierarchical Bayesian Kalman filter based approach
Hierarchical Bayesian Kalman filter (HBKF) based schemes are conceived for doubly-selective sparse channel estimation in orthogonal space-time block coded (OSTBC) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) wireless systems. Initially, a pilot based multiple measurement vector (MMV) model is formulated for estimating the OSTBC MIMO-OFDM channel. This is followed by the development of a low-complexity, online pilot-based HBKF (P-HBKF) scheme for tracking the sparse time-varying frequency-selective channel. The salient advantages of the proposed P-HBKF technique are that it requires significantly lower number of pilot subcarriers, while also exploiting the inherent sparsity of the wireless channel. Subsequently, data detection is also incorporated in the proposed framework, leading to the development of a procedure for joint sparse doubly-selective channel estimation and symbol detection. Recursive Bayesian Cramér-Rao bounds and closed form expressions are also obtained for the asymptotic mean square error (MSE) based on the solution of the Riccati equation for the KF for benchmarking the performance. Simulation results are presented for validating the theoretical bounds and for comparing the performance of the proposed and existing techniques.
Cramér-Rao bound, MIMO-OFDM, OSTBC, Riccati equation, channel estimation, frequency-selective, hierarchical Bayesian Kalman filter, sparse Bayesian learning, sparsity
4844-4858
Srivastava, Suraj
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Kumar, Mahendrada Sarath
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Mishra, Amrita
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Chopra, Sanjana
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Jagannatham, Aditya K.
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Hanzo, Lajos
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August 2020
Srivastava, Suraj
7b40cb6c-7bc6-402c-8751-24346d39002c
Kumar, Mahendrada Sarath
6b516644-295a-4fe6-9ea4-58454a638f87
Mishra, Amrita
7f79fb72-5449-4597-8460-e72ee62f9005
Chopra, Sanjana
d5c551e7-6e2b-4ad3-857f-e3e76042ed54
Jagannatham, Aditya K.
ea2f628b-0f2a-48a3-a293-122c809757aa
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Srivastava, Suraj, Kumar, Mahendrada Sarath, Mishra, Amrita, Chopra, Sanjana, Jagannatham, Aditya K. and Hanzo, Lajos
(2020)
Sparse doubly-selective channel estimation techniques for OSTBC MIMO-OFDM systems: a hierarchical Bayesian Kalman filter based approach.
IEEE Transactions on Communications, 68 (8), , [9096402].
(doi:10.1109/TCOMM.2020.2995585).
Abstract
Hierarchical Bayesian Kalman filter (HBKF) based schemes are conceived for doubly-selective sparse channel estimation in orthogonal space-time block coded (OSTBC) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) wireless systems. Initially, a pilot based multiple measurement vector (MMV) model is formulated for estimating the OSTBC MIMO-OFDM channel. This is followed by the development of a low-complexity, online pilot-based HBKF (P-HBKF) scheme for tracking the sparse time-varying frequency-selective channel. The salient advantages of the proposed P-HBKF technique are that it requires significantly lower number of pilot subcarriers, while also exploiting the inherent sparsity of the wireless channel. Subsequently, data detection is also incorporated in the proposed framework, leading to the development of a procedure for joint sparse doubly-selective channel estimation and symbol detection. Recursive Bayesian Cramér-Rao bounds and closed form expressions are also obtained for the asymptotic mean square error (MSE) based on the solution of the Riccati equation for the KF for benchmarking the performance. Simulation results are presented for validating the theoretical bounds and for comparing the performance of the proposed and existing techniques.
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final manuscript
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More information
Accepted/In Press date: 9 May 2020
e-pub ahead of print date: 19 May 2020
Published date: August 2020
Additional Information:
Funding Information:
Manuscript received September 26, 2019; revised February 13, 2020 and April 14, 2020; accepted May 9, 2020. Date of publication May 19, 2020; date of current version August 14, 2020. The work of A. K. Jagannatham has been supported in part by the IIMA IDEA Telecom Centre of Excellence (IITCOE) and Qualcomm Innovation Fellowship (QIF). 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. The associate editor coordinating the review of this article and approving it for publication was B. Shim. (Corresponding author: Lajos Hanzo.) Suraj Srivastava and Aditya K. Jagannatham are with the Department of Electrical Engineering, IIT Kanpur, Kanpur 208016, India (e-mail: ssrivast@iitk.ac.in; adityaj@iitk.ac.in).
Publisher Copyright:
© 2020 IEEE.
Keywords:
Cramér-Rao bound, MIMO-OFDM, OSTBC, Riccati equation, channel estimation, frequency-selective, hierarchical Bayesian Kalman filter, sparse Bayesian learning, sparsity
Identifiers
Local EPrints ID: 441012
URI: http://eprints.soton.ac.uk/id/eprint/441012
ISSN: 0090-6778
PURE UUID: 51e92dda-5301-4241-b184-f0458f3fa1c6
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Date deposited: 27 May 2020 16:54
Last modified: 18 Mar 2024 02:36
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Contributors
Author:
Suraj Srivastava
Author:
Mahendrada Sarath Kumar
Author:
Amrita Mishra
Author:
Sanjana Chopra
Author:
Aditya K. Jagannatham
Author:
Lajos Hanzo
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