Hybrid transceiver design for Tera-Hertz MIMO systems relying on Bayesian learning aided sparse channel estimation
Hybrid transceiver design for Tera-Hertz MIMO systems relying on Bayesian learning aided sparse channel estimation
Hybrid transceiver design in multiple-input multiple-output (MIMO) Tera-Hertz (THz) systems relying on sparse channel state information (CSI) estimation techniques is conceived. To begin with, a practical MIMO channel model is developed for the THz band that incorporates its molecular absorption and reflection losses, as well as its non-line-ofsight (NLoS) rays associated with its diffused components. Subsequently, a novel CSI estimation model is derived by exploiting the angular-sparsity of the THz MIMO channel. This is followed by designing a sophisticated Bayesian learning (BL)-based approach for efficient estimation of the sparse THz MIMO channel. The Bayesian Cramer-Rao Lower Bound (BCRLB) is also determined for benchmarking the performance of the CSI estimation techniques developed. Finally, an optimal hybrid transmit precoder and receiver combiner pair is designed, which directly relies on the beamspace domain CSI estimates and only requires limited feedback. Finally, simulation results are provided for quantifying the improved mean square error (MSE), spectral-efficiency (SE) and bit-error rate (BER) performance for transmission on practical THz MIMO channel obtained from the HIgh resolution TRANsmission (HITRAN)-database.
Absorption, Antenna arrays, Bayesian learning, Channel estimation, Estimation, HITRAN-database, MIMO communication, Radio frequency, Transceivers, beamspace representation, hybrid MIMO systems, molecular absorption, sparse channel estimation, tera-Hertz communication, transceiver design
1
Srivastava, Suraj
7b40cb6c-7bc6-402c-8751-24346d39002c
Tripathi, Ajeet
96db28cf-4695-40a1-b90d-1143205173a3
Varshney, Neeraj
3b848e7d-b733-4a00-9942-62ced993f0f0
Jagannatham, Aditya K.
ea2f628b-0f2a-48a3-a293-122c809757aa
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
5 October 2022
Srivastava, Suraj
7b40cb6c-7bc6-402c-8751-24346d39002c
Tripathi, Ajeet
96db28cf-4695-40a1-b90d-1143205173a3
Varshney, Neeraj
3b848e7d-b733-4a00-9942-62ced993f0f0
Jagannatham, Aditya K.
ea2f628b-0f2a-48a3-a293-122c809757aa
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Srivastava, Suraj, Tripathi, Ajeet, Varshney, Neeraj, Jagannatham, Aditya K. and Hanzo, Lajos
(2022)
Hybrid transceiver design for Tera-Hertz MIMO systems relying on Bayesian learning aided sparse channel estimation.
IEEE Transactions on Wireless Communications, .
(doi:10.1109/TWC.2022.3210306).
Abstract
Hybrid transceiver design in multiple-input multiple-output (MIMO) Tera-Hertz (THz) systems relying on sparse channel state information (CSI) estimation techniques is conceived. To begin with, a practical MIMO channel model is developed for the THz band that incorporates its molecular absorption and reflection losses, as well as its non-line-ofsight (NLoS) rays associated with its diffused components. Subsequently, a novel CSI estimation model is derived by exploiting the angular-sparsity of the THz MIMO channel. This is followed by designing a sophisticated Bayesian learning (BL)-based approach for efficient estimation of the sparse THz MIMO channel. The Bayesian Cramer-Rao Lower Bound (BCRLB) is also determined for benchmarking the performance of the CSI estimation techniques developed. Finally, an optimal hybrid transmit precoder and receiver combiner pair is designed, which directly relies on the beamspace domain CSI estimates and only requires limited feedback. Finally, simulation results are provided for quantifying the improved mean square error (MSE), spectral-efficiency (SE) and bit-error rate (BER) performance for transmission on practical THz MIMO channel obtained from the HIgh resolution TRANsmission (HITRAN)-database.
Text
Hybrid Transceiver Design for Tera-Hertz MIMO Systems Relying on Bayesian Learning Aided Sparse Channel Estimation
- Accepted Manuscript
More information
Accepted/In Press date: 25 September 2022
Published date: 5 October 2022
Additional Information:
Publisher Copyright:
IEEE
Keywords:
Absorption, Antenna arrays, Bayesian learning, Channel estimation, Estimation, HITRAN-database, MIMO communication, Radio frequency, Transceivers, beamspace representation, hybrid MIMO systems, molecular absorption, sparse channel estimation, tera-Hertz communication, transceiver design
Identifiers
Local EPrints ID: 470899
URI: http://eprints.soton.ac.uk/id/eprint/470899
ISSN: 1536-1276
PURE UUID: 7557c518-9a3f-448c-9ad6-d1601a7c82cf
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Date deposited: 20 Oct 2022 16:51
Last modified: 18 Mar 2024 02:36
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Contributors
Author:
Suraj Srivastava
Author:
Ajeet Tripathi
Author:
Neeraj Varshney
Author:
Aditya K. Jagannatham
Author:
Lajos Hanzo
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