The University of Southampton
University of Southampton Institutional Repository

Joint sparsity pattern learning based channel estimation for massive MIMO-OTFS systems

Joint sparsity pattern learning based channel estimation for massive MIMO-OTFS systems
Joint sparsity pattern learning based channel estimation for massive MIMO-OTFS systems
We propose a channel estimation scheme based on joint sparsity pattern learning (JSPL) for massive multi-input multi-output (MIMO) orthogonal time-frequency-space (OTFS) modulation aided systems. By exploiting the potential joint sparsity of the delay-Doppler-angle (DDA) domain channel, the channel estimation problem is transformed into a sparse recovery problem. To solve it, we first apply the spike and slab prior model to iteratively estimate the support set of the channel matrix, and a higher-accuracy parameter update rule relying on the identified support set is introduced into the iteration. Then the specific values of the channel elements corresponding to the support set are estimated by the orthogonal matching pursuit (OMP) method. Both our simulation results and analysis demonstrate that the proposed JSPL channel estimation scheme achieves an improved performance over the representative state-of-the-art baseline schemes, despite its reduced pilot overhead.
0018-9545
Meng, Kuo
f54440e2-23b8-4b7f-a291-9b4d75adee82
Yang, Shaoshi
376b9d24-235e-49b6-bf17-302260f7e2be
Wang, Xiao-Yang
6f6e5675-af49-4c06-8abf-2a45a9427fb8
Bu, Yan
cd927263-f468-483d-abcb-3de44bf26251
Tang, Yurong
729b10eb-6d2f-4e57-8680-8decf97155b6
Zhang, Jinhua
fc9b1f90-33b2-4381-ac93-57e7c79fc10c
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Meng, Kuo
f54440e2-23b8-4b7f-a291-9b4d75adee82
Yang, Shaoshi
376b9d24-235e-49b6-bf17-302260f7e2be
Wang, Xiao-Yang
6f6e5675-af49-4c06-8abf-2a45a9427fb8
Bu, Yan
cd927263-f468-483d-abcb-3de44bf26251
Tang, Yurong
729b10eb-6d2f-4e57-8680-8decf97155b6
Zhang, Jinhua
fc9b1f90-33b2-4381-ac93-57e7c79fc10c
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1

Meng, Kuo, Yang, Shaoshi, Wang, Xiao-Yang, Bu, Yan, Tang, Yurong, Zhang, Jinhua and Hanzo, Lajos (2024) Joint sparsity pattern learning based channel estimation for massive MIMO-OTFS systems. IEEE Transactions on Vehicular Technology, 73 (8). (doi:10.1109/TVT.2024.3375027).

Record type: Article

Abstract

We propose a channel estimation scheme based on joint sparsity pattern learning (JSPL) for massive multi-input multi-output (MIMO) orthogonal time-frequency-space (OTFS) modulation aided systems. By exploiting the potential joint sparsity of the delay-Doppler-angle (DDA) domain channel, the channel estimation problem is transformed into a sparse recovery problem. To solve it, we first apply the spike and slab prior model to iteratively estimate the support set of the channel matrix, and a higher-accuracy parameter update rule relying on the identified support set is introduced into the iteration. Then the specific values of the channel elements corresponding to the support set are estimated by the orthogonal matching pursuit (OMP) method. Both our simulation results and analysis demonstrate that the proposed JSPL channel estimation scheme achieves an improved performance over the representative state-of-the-art baseline schemes, despite its reduced pilot overhead.

Text
channelestimation - Accepted Manuscript
Download (748kB)

More information

Accepted/In Press date: 5 March 2024
e-pub ahead of print date: 18 March 2024
Published date: August 2024

Identifiers

Local EPrints ID: 487868
URI: http://eprints.soton.ac.uk/id/eprint/487868
ISSN: 0018-9545
PURE UUID: 615521fd-7a55-4702-923f-1f7605ff9888
ORCID for Lajos Hanzo: ORCID iD orcid.org/0000-0002-2636-5214

Catalogue record

Date deposited: 07 Mar 2024 17:44
Last modified: 03 Oct 2025 04:01

Export record

Altmetrics

Contributors

Author: Kuo Meng
Author: Shaoshi Yang
Author: Xiao-Yang Wang
Author: Yan Bu
Author: Yurong Tang
Author: Jinhua Zhang
Author: Lajos Hanzo ORCID iD

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

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×