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Bayesian learning-based linear decentralized sparse parameter estimation in MIMO wireless sensor networks relying on imperfect CSI

Bayesian learning-based linear decentralized sparse parameter estimation in MIMO wireless sensor networks relying on imperfect CSI
Bayesian learning-based linear decentralized sparse parameter estimation in MIMO wireless sensor networks relying on imperfect CSI
Optimal linear minimum mean square error (MMSE) transceiver design techniques are proposed for Bayesian learning (BL)-based sparse parameter vector estimation in a multiple-input multiple-output (MIMO) wireless sensor network (WSN). Our proposed transceiver designs rely on majorization theory and hyperparameter estimates obtained from the BL module for minimizing the mean square error (MSE) of parameter estimation at the fusion center (FC). The linear transceiver design framework is initially proposed for the general scenario with arbitrary SNR sensor observations, followed by a special case with high-SNR sensor observations scenario. Our analysis also incorporates the channel correlation. The MMSE channel estimates are determined for the sensors (SNs), followed by a robust transceiver design procedure that is resilient to the channel state information (CSI) uncertainty arising due to the channel estimation error, an aberration that is unavoidable in practical implementations. Our simulation results demonstrate the improved performance of the proposed BL framework and optimal MMSE transceiver design in sparse parameter estimation relyingon realistic imperfect channel estimates over the benchmarks.
Bayesian learning (BL), decentralized estimation, multiple access channel (MAC), Sensor networks, sparse parameter estimation, stochastic CSI uncertainty, transceiver design
0090-6778
6236-6250
Rajput, Kunwar Pritiraj
fe656d56-6b0a-4798-9d04-60650d95fb74
Kumar, Abhishek
23078539-c9f9-4955-96ff-3ba9d1bd8bf5
Srivastava, Suraj
7b40cb6c-7bc6-402c-8751-24346d39002c
Jagannatham, Aditya K.
ea2f628b-0f2a-48a3-a293-122c809757aa
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Rajput, Kunwar Pritiraj
fe656d56-6b0a-4798-9d04-60650d95fb74
Kumar, Abhishek
23078539-c9f9-4955-96ff-3ba9d1bd8bf5
Srivastava, Suraj
7b40cb6c-7bc6-402c-8751-24346d39002c
Jagannatham, Aditya K.
ea2f628b-0f2a-48a3-a293-122c809757aa
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1

Rajput, Kunwar Pritiraj, Kumar, Abhishek, Srivastava, Suraj, Jagannatham, Aditya K. and Hanzo, Lajos (2021) Bayesian learning-based linear decentralized sparse parameter estimation in MIMO wireless sensor networks relying on imperfect CSI. IEEE Transactions on Communications, 69 (9), 6236-6250. (doi:10.1109/TCOMM.2021.3091181).

Record type: Article

Abstract

Optimal linear minimum mean square error (MMSE) transceiver design techniques are proposed for Bayesian learning (BL)-based sparse parameter vector estimation in a multiple-input multiple-output (MIMO) wireless sensor network (WSN). Our proposed transceiver designs rely on majorization theory and hyperparameter estimates obtained from the BL module for minimizing the mean square error (MSE) of parameter estimation at the fusion center (FC). The linear transceiver design framework is initially proposed for the general scenario with arbitrary SNR sensor observations, followed by a special case with high-SNR sensor observations scenario. Our analysis also incorporates the channel correlation. The MMSE channel estimates are determined for the sensors (SNs), followed by a robust transceiver design procedure that is resilient to the channel state information (CSI) uncertainty arising due to the channel estimation error, an aberration that is unavoidable in practical implementations. Our simulation results demonstrate the improved performance of the proposed BL framework and optimal MMSE transceiver design in sparse parameter estimation relyingon realistic imperfect channel estimates over the benchmarks.

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Accepted/In Press date: 13 June 2021
Published date: September 2021
Additional Information: Funding Information: Manuscript received December 9, 2020; revised April 19, 2021; accepted June 12, 2021. Date of publication June 21, 2021; date of current version September 16, 2021. Lajos Hanzo would like to acknowledge the financial support of the Engineering and Physical Sciences Research Council Projects EP/P034284/1 and EP/P003990/1 (COALESCE) as well as of the European Research Council’s Advanced Fellow Grant QuantCom (Grant No. 789028). Aditya K. Jagannatham would like to acknowledge that this research is supported in part by the Science and Engineering Research Board (SERB), Department of Science and Technology, Government of India, in part by the Space Technology Cell, IIT Kanpur, in part by the IIMA IDEA Telecom Centre of Excellence, in part by the Qualcomm Innovation Fellowship, and in part by the Arun Kumar Chair Professorship. The associate editor coordinating the review of this article and approving it for publication was L. Wei. (Corresponding author: Lajos Hanzo.) Kunwar Pritiraj Rajput, Suraj Srivastava, and Aditya K. Jagannatham are with the Department of Electrical Engineering, Indian Institute of Technology, Kanpur, Kanpur 208016, India (e-mail: pratiraj@iitk.ac.in; ssrivast@iitk.ac.in; adityaj@iitk.ac.in). Publisher Copyright: © 1972-2012 IEEE. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.
Keywords: Bayesian learning (BL), decentralized estimation, multiple access channel (MAC), Sensor networks, sparse parameter estimation, stochastic CSI uncertainty, transceiver design

Identifiers

Local EPrints ID: 449881
URI: http://eprints.soton.ac.uk/id/eprint/449881
ISSN: 0090-6778
PURE UUID: 307f2abc-9a96-4285-9872-0f207af33712
ORCID for Lajos Hanzo: ORCID iD orcid.org/0000-0002-2636-5214

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Date deposited: 23 Jun 2021 16:31
Last modified: 18 Mar 2024 05:27

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Contributors

Author: Kunwar Pritiraj Rajput
Author: Abhishek Kumar
Author: Suraj Srivastava
Author: Aditya K. Jagannatham
Author: Lajos Hanzo ORCID iD

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