Bayesian learning aided sparse channel estimation for orthogonal time frequency space modulated systems
Bayesian learning aided sparse channel estimation for orthogonal time frequency space modulated systems
A novel sparse channel state information (CSI) estimation scheme is proposed for orthogonal time frequency space (OTFS) modulated systems, in which the pilots are directly transmitted over the time-frequency (TF)-domain grid for estimating the delay-Doppler (DD)-domain CSI. The proposed CSI estimation model leads to a reduction in the pilot overhead as well as the training duration required. Furthermore, it does not require a DD-domain guard interval between the pilot and data symbols, hence increasing the bandwidth efficiency. A novel Bayesian learning (BL) framework is proposed for CSI acquisition, which exploits the DD-domain sparsity for improving the estimation accuracy in comparison to the conventional minimum mean squared error (MMSE)-based scheme. A low complexity linear MMSE detector is used in the subsequent data detection phase. Our simulation results demonstrate the performance improvement of the proposed BL-based scheme over the conventional MMSE-based scheme as well as over other existing sparse estimation schemes.
Srivastava, Suraj
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Kumar Singh, Rahul
5bf0c2f9-c5c0-426f-b9dc-8ee278f0afe8
Jagannatham, Aditya K.
ae9274e6-c98c-4e15-a5be-f4eb0fc179ff
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Srivastava, Suraj
a90b79db-5004-4786-9e40-995bd5ce2606
Kumar Singh, Rahul
5bf0c2f9-c5c0-426f-b9dc-8ee278f0afe8
Jagannatham, Aditya K.
ae9274e6-c98c-4e15-a5be-f4eb0fc179ff
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Srivastava, Suraj, Kumar Singh, Rahul, Jagannatham, Aditya K. and Hanzo, Lajos
(2021)
Bayesian learning aided sparse channel estimation for orthogonal time frequency space modulated systems.
IEEE Transactions on Vehicular Technology.
(In Press)
Abstract
A novel sparse channel state information (CSI) estimation scheme is proposed for orthogonal time frequency space (OTFS) modulated systems, in which the pilots are directly transmitted over the time-frequency (TF)-domain grid for estimating the delay-Doppler (DD)-domain CSI. The proposed CSI estimation model leads to a reduction in the pilot overhead as well as the training duration required. Furthermore, it does not require a DD-domain guard interval between the pilot and data symbols, hence increasing the bandwidth efficiency. A novel Bayesian learning (BL) framework is proposed for CSI acquisition, which exploits the DD-domain sparsity for improving the estimation accuracy in comparison to the conventional minimum mean squared error (MMSE)-based scheme. A low complexity linear MMSE detector is used in the subsequent data detection phase. Our simulation results demonstrate the performance improvement of the proposed BL-based scheme over the conventional MMSE-based scheme as well as over other existing sparse estimation schemes.
Text
Bayesian Learning Aided Sparse Channel Estimation for Orthogonal Time Frequency Space Modulated Systems
- Accepted Manuscript
More information
Accepted/In Press date: July 2021
Identifiers
Local EPrints ID: 450287
URI: http://eprints.soton.ac.uk/id/eprint/450287
ISSN: 0018-9545
PURE UUID: 5e9aa05e-ad76-434a-8e23-7d4019ece299
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Date deposited: 20 Jul 2021 16:32
Last modified: 17 Mar 2024 02:35
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Contributors
Author:
Suraj Srivastava
Author:
Rahul Kumar Singh
Author:
Aditya K. Jagannatham
Author:
Lajos Hanzo
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