Robust linear decentralized tracking of a time-varying sparse parameter relying on imperfect CSI
Robust linear decentralized tracking of a time-varying sparse parameter relying on imperfect CSI
Robust linear decentralized tracking of a time-varying sparse parameter is studied in a multiple-input-multiple-output (MIMO) wireless sensor network (WSN) under channel state information (CSI) uncertainty. Initially, assuming perfect CSI availability, a novel sparse Bayesian learning-based Kalman filtering (SBL-KF) framework is developed in order to track the time-varying sparse parameter. Subsequently, an optimization problem is formulated to minimize the mean-square error (MSE) in each time slot (TS), followed by the design of a fast block coordinate descent (FBCD)-based iterative algorithm. A unique aspect of the proposed technique is that it requires only a single iteration per TS to obtain the transmit precoder (TPC) matrices for all the sensor nodes (SNs) and the receiver combiner (RC) matrix for the fusion center (FC) in an online fashion. The recursive Bayesian Cramer-Rao bound (BCRB) is also derived for benchmarking the performance of the proposed linear decentralized estimation (LDE) scheme. Furthermore, for considering a practical scenario having CSI uncertainty, a robust SBL-KF (RSBL-KF) is derived for tracking the unknown parameter vector of interest followed by the conception of a robust transceiver design. Our simulation results show that the schemes designed outperform both the traditional sparsity-agnostic Kalman filter and the state-of-the-art sparse reconstruction methods. Furthermore, as compared to the uncertainty-agnostic design, the robust transceiver architecture conceived is shown to provide improved estimation performance, making it eminently suitable for practical applications.
Bayes methods, Coherent MAC, Estimation, Internet of Things, Kalman filter, Minimization, Parameter estimation, Transceivers, Wireless sensor networks, linear decentralized estimation, sparse Bayesian learning, stochastic CSI uncertainty, time varying sparse parameter, wireless sensor network, sparse Bayesian learning (SBL), Coherent multiple access channel (MAC), Kalman filter (KF), stochastic channel state information (CSI) uncertainty, time-varying sparse parameter, wireless sensor network (WSN), linear decentralized estimation (LDE)
16156-16168
Rajput, Kunwar Pritiraj
fe656d56-6b0a-4798-9d04-60650d95fb74
Srivastava, Suraj
10635d73-e2d1-409c-95e6-0638da7b900b
K. Jagannatham, Aditya
aee5dcc4-5537-43b1-8e18-81552dc93534
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
15 September 2023
Rajput, Kunwar Pritiraj
fe656d56-6b0a-4798-9d04-60650d95fb74
Srivastava, Suraj
10635d73-e2d1-409c-95e6-0638da7b900b
K. Jagannatham, Aditya
aee5dcc4-5537-43b1-8e18-81552dc93534
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Rajput, Kunwar Pritiraj, Srivastava, Suraj, K. Jagannatham, Aditya and Hanzo, Lajos
(2023)
Robust linear decentralized tracking of a time-varying sparse parameter relying on imperfect CSI.
IEEE Internet of Things Journal, 10 (18), .
(doi:10.1109/JIOT.2023.3267368).
Abstract
Robust linear decentralized tracking of a time-varying sparse parameter is studied in a multiple-input-multiple-output (MIMO) wireless sensor network (WSN) under channel state information (CSI) uncertainty. Initially, assuming perfect CSI availability, a novel sparse Bayesian learning-based Kalman filtering (SBL-KF) framework is developed in order to track the time-varying sparse parameter. Subsequently, an optimization problem is formulated to minimize the mean-square error (MSE) in each time slot (TS), followed by the design of a fast block coordinate descent (FBCD)-based iterative algorithm. A unique aspect of the proposed technique is that it requires only a single iteration per TS to obtain the transmit precoder (TPC) matrices for all the sensor nodes (SNs) and the receiver combiner (RC) matrix for the fusion center (FC) in an online fashion. The recursive Bayesian Cramer-Rao bound (BCRB) is also derived for benchmarking the performance of the proposed linear decentralized estimation (LDE) scheme. Furthermore, for considering a practical scenario having CSI uncertainty, a robust SBL-KF (RSBL-KF) is derived for tracking the unknown parameter vector of interest followed by the conception of a robust transceiver design. Our simulation results show that the schemes designed outperform both the traditional sparsity-agnostic Kalman filter and the state-of-the-art sparse reconstruction methods. Furthermore, as compared to the uncertainty-agnostic design, the robust transceiver architecture conceived is shown to provide improved estimation performance, making it eminently suitable for practical applications.
Text
Robust Linear Decentralized Tracking of a Time-Varying Sparse Parameter Relying on Imperfect CSI
- Accepted Manuscript
More information
Accepted/In Press date: 12 April 2023
e-pub ahead of print date: 14 April 2023
Published date: 15 September 2023
Additional Information:
Publisher Copyright:
IEEE
Keywords:
Bayes methods, Coherent MAC, Estimation, Internet of Things, Kalman filter, Minimization, Parameter estimation, Transceivers, Wireless sensor networks, linear decentralized estimation, sparse Bayesian learning, stochastic CSI uncertainty, time varying sparse parameter, wireless sensor network, sparse Bayesian learning (SBL), Coherent multiple access channel (MAC), Kalman filter (KF), stochastic channel state information (CSI) uncertainty, time-varying sparse parameter, wireless sensor network (WSN), linear decentralized estimation (LDE)
Identifiers
Local EPrints ID: 476703
URI: http://eprints.soton.ac.uk/id/eprint/476703
ISSN: 2327-4662
PURE UUID: 66eed4f2-ceda-44ba-b0ab-1a4af7918585
Catalogue record
Date deposited: 11 May 2023 16:59
Last modified: 18 Mar 2024 02:36
Export record
Altmetrics
Contributors
Author:
Kunwar Pritiraj Rajput
Author:
Suraj Srivastava
Author:
Aditya K. Jagannatham
Author:
Lajos Hanzo
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics