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Sparse Bayesian learning aided estimation of doubly-selective MIMO channels for filter bank multicarrier systems

Sparse Bayesian learning aided estimation of doubly-selective MIMO channels for filter bank multicarrier systems
Sparse Bayesian learning aided estimation of doubly-selective MIMO channels for filter bank multicarrier systems
Sparse Bayesian learning (SBL)-based channel state information (CSI) estimation schemes are developed for filter bank multicarrier (FBMC) systems using offset quadrature amplitude modulation (OQAM). Initially, an SBL-based channel estimation scheme is designed for a frequency-selective quasistatic single-input single-output (SISO)-FBMC system, relying on the interference approximation method (IAM). The IAM technique, although has low complexity, is only suitable for channels exhibiting mild frequency-selectivity. Hence, an alternative time-domain (TD) model based sparse channel estimation framework is developed for highly frequency-selective channels. Subsequently, the Kalman filtering (KF)-based IAM and its TD counterpart are developed for sparse doubly-selective CSI estimation in SISO-FBMC systems. These schemes are also extended to FBMC-based multiple-input multiple-output (MIMO) systems, for both quasi-static and doubly-selective channels, after demonstrating the special block and group-sparse structures of the IAM and TD-based models respectively, which are the characteristic features of such channels. The Bayesian Cramér-Rao lower bounds (BCRLBs) and the time-recursive BCRLBs are derived for the proposed quasi-static as well as doubly-selective sparse CSI estimation models, respectively. Our numerical results closely match the analytical findings, demonstrating the enhanced performance of the proposed schemes over the existing techniques.
Channel estimation, Filter bank multicarrier, Kalman filtering, Sparse Bayesian learning
0090-6778
4236-4249
Singh, Prem
b3155131-3d31-4d82-876c-33655420c7e5
Srivastava, Suraj
7b40cb6c-7bc6-402c-8751-24346d39002c
Mishra, Amrita
7f79fb72-5449-4597-8460-e72ee62f9005
Jagannatham, Aditya K.
ea2f628b-0f2a-48a3-a293-122c809757aa
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Singh, Prem
b3155131-3d31-4d82-876c-33655420c7e5
Srivastava, Suraj
7b40cb6c-7bc6-402c-8751-24346d39002c
Mishra, Amrita
7f79fb72-5449-4597-8460-e72ee62f9005
Jagannatham, Aditya K.
ea2f628b-0f2a-48a3-a293-122c809757aa
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1

Singh, Prem, Srivastava, Suraj, Mishra, Amrita, Jagannatham, Aditya K. and Hanzo, Lajos (2022) Sparse Bayesian learning aided estimation of doubly-selective MIMO channels for filter bank multicarrier systems. IEEE Transactions on Communications, 70 (6), 4236-4249. (doi:10.1109/TCOMM.2022.3171815).

Record type: Article

Abstract

Sparse Bayesian learning (SBL)-based channel state information (CSI) estimation schemes are developed for filter bank multicarrier (FBMC) systems using offset quadrature amplitude modulation (OQAM). Initially, an SBL-based channel estimation scheme is designed for a frequency-selective quasistatic single-input single-output (SISO)-FBMC system, relying on the interference approximation method (IAM). The IAM technique, although has low complexity, is only suitable for channels exhibiting mild frequency-selectivity. Hence, an alternative time-domain (TD) model based sparse channel estimation framework is developed for highly frequency-selective channels. Subsequently, the Kalman filtering (KF)-based IAM and its TD counterpart are developed for sparse doubly-selective CSI estimation in SISO-FBMC systems. These schemes are also extended to FBMC-based multiple-input multiple-output (MIMO) systems, for both quasi-static and doubly-selective channels, after demonstrating the special block and group-sparse structures of the IAM and TD-based models respectively, which are the characteristic features of such channels. The Bayesian Cramér-Rao lower bounds (BCRLBs) and the time-recursive BCRLBs are derived for the proposed quasi-static as well as doubly-selective sparse CSI estimation models, respectively. Our numerical results closely match the analytical findings, demonstrating the enhanced performance of the proposed schemes over the existing techniques.

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More information

Accepted/In Press date: 26 April 2022
e-pub ahead of print date: 2 May 2022
Published date: 1 June 2022
Keywords: Channel estimation, Filter bank multicarrier, Kalman filtering, Sparse Bayesian learning

Identifiers

Local EPrints ID: 457167
URI: http://eprints.soton.ac.uk/id/eprint/457167
ISSN: 0090-6778
PURE UUID: 6aebf566-3468-4a1a-9bf2-cc8f68d42d78
ORCID for Lajos Hanzo: ORCID iD orcid.org/0000-0002-2636-5214

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Date deposited: 25 May 2022 16:54
Last modified: 18 Mar 2024 02:36

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Contributors

Author: Prem Singh
Author: Suraj Srivastava
Author: Amrita Mishra
Author: Aditya K. Jagannatham
Author: Lajos Hanzo ORCID iD

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