Hybrid near- and far-field THz UM-MIMO channel estimation: a sparsifying matrix learning-aided Bayesian approach
Channel estimation (CE) is a critical challenge in harnessing the potential of Terahertz (THz) ultra-massive multiple-input multiple-output (UM-MIMO) systems. Sparsity-exploiting compressed sensing (CS)-aided CE (CSCE) can enhance THz UM-MIMO CE performance with affordable pilot overhead. However, t...
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sg-ntu-dr.10356-1818072024-12-20T00:06:41Z Hybrid near- and far-field THz UM-MIMO channel estimation: a sparsifying matrix learning-aided Bayesian approach Li, Yuanjian Madhukumar, A. S. College of Computing and Data Science Engineering Terahertz communications Ultra-massive multiple-input multiple-output systems Channel estimation Compressed sensing Dictionary learning Channel estimation (CE) is a critical challenge in harnessing the potential of Terahertz (THz) ultra-massive multiple-input multiple-output (UM-MIMO) systems. Sparsity-exploiting compressed sensing (CS)-aided CE (CSCE) can enhance THz UM-MIMO CE performance with affordable pilot overhead. However, the near-field propagation region becomes significant in THz UM-MIMO networks due to the large array aperture and high carrier frequency, leading to a more profound coexistence of near- and far-field radiation patterns. This hybrid-field propagation characteristic renders existing CSCE frameworks ineffective due to the lack of an appropriate sparsifying matrix. In this work, we investigate the uplink THz UM-MIMO CE problem, by developing a practical THz UM-MIMO channel model that incorporates near- and far-field paths, molecular absorption, and reflection attenuation. We propose a dictionary learning (DL)-aided Bayesian THz CSCE solution to achieve accurate, robust and pilot-efficient CE, even in ill-posed scenarios. Specifically, we tailor a batch-delayed online DL (BD-ODL) algorithm to generate an appropriate dictionary for the hybrid-field THz UM-MIMO channel model. Furthermore, we propose a Bayesian learning (BL)-enabled CSCE framework to leverage THz sparsity and utilize the learnt dictionary. To establish a lower bound for the mean squared error (MSE), we derive the Bayesian Cramér-Rao bound (BCRB). We also conduct a complexity analysis to quantify the required computational resources. Numerical results show a significant improvement in normalized MSE (NMSE) performance compared to conventional CE and CSCE baselines, and demonstrate rapid convergence. Info-communications Media Development Authority (IMDA) National Research Foundation (NRF) This research is supported by the National Research Foundation, Singapore and Infocomm Media Development Authority under its Future Communications Research & Development Programme (FCP-NTU-RG-2022-014), and by National Research Foundation, Singapore under its Competitive Research Programme (NRF-CRP23-2019-0005). 2024-12-20T00:06:41Z 2024-12-20T00:06:41Z 2024 Journal Article Li, Y. & Madhukumar, A. S. (2024). Hybrid near- and far-field THz UM-MIMO channel estimation: a sparsifying matrix learning-aided Bayesian approach. IEEE Transactions On Wireless Communications. https://dx.doi.org/10.1109/TWC.2024.3514141 1536-1276 https://hdl.handle.net/10356/181807 10.1109/TWC.2024.3514141 en FCP-NTU-RG-2022-014 NRF-CRP23-2019-0005 IEEE Transactions on Wireless Communications © 2024 IEEE. All rights reserved. |
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Engineering Terahertz communications Ultra-massive multiple-input multiple-output systems Channel estimation Compressed sensing Dictionary learning |
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Engineering Terahertz communications Ultra-massive multiple-input multiple-output systems Channel estimation Compressed sensing Dictionary learning Li, Yuanjian Madhukumar, A. S. Hybrid near- and far-field THz UM-MIMO channel estimation: a sparsifying matrix learning-aided Bayesian approach |
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Channel estimation (CE) is a critical challenge in harnessing the potential of Terahertz (THz) ultra-massive multiple-input multiple-output (UM-MIMO) systems. Sparsity-exploiting compressed sensing (CS)-aided CE (CSCE) can enhance THz UM-MIMO CE performance with affordable pilot overhead. However, the near-field propagation region becomes significant in THz UM-MIMO networks due to the large array aperture and high carrier frequency, leading to a more profound coexistence of near- and far-field radiation patterns. This hybrid-field propagation characteristic renders existing CSCE frameworks ineffective due to the lack of an appropriate sparsifying matrix. In this work, we investigate the uplink THz UM-MIMO CE problem, by developing a practical THz UM-MIMO channel model that incorporates near- and far-field paths, molecular absorption, and reflection attenuation. We propose a dictionary learning (DL)-aided Bayesian THz CSCE solution to achieve accurate, robust and pilot-efficient CE, even in ill-posed scenarios. Specifically, we tailor a batch-delayed online DL (BD-ODL) algorithm to generate an appropriate dictionary for the hybrid-field THz UM-MIMO channel model. Furthermore, we propose a Bayesian learning (BL)-enabled CSCE framework to leverage THz sparsity and utilize the learnt dictionary. To establish a lower bound for the mean squared error (MSE), we derive the Bayesian Cramér-Rao bound (BCRB). We also conduct a complexity analysis to quantify the required computational resources. Numerical results show a significant improvement in normalized MSE (NMSE) performance compared to conventional CE and CSCE baselines, and demonstrate rapid convergence. |
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College of Computing and Data Science |
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College of Computing and Data Science Li, Yuanjian Madhukumar, A. S. |
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author |
Li, Yuanjian Madhukumar, A. S. |
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Li, Yuanjian |
title |
Hybrid near- and far-field THz UM-MIMO channel estimation: a sparsifying matrix learning-aided Bayesian approach |
title_short |
Hybrid near- and far-field THz UM-MIMO channel estimation: a sparsifying matrix learning-aided Bayesian approach |
title_full |
Hybrid near- and far-field THz UM-MIMO channel estimation: a sparsifying matrix learning-aided Bayesian approach |
title_fullStr |
Hybrid near- and far-field THz UM-MIMO channel estimation: a sparsifying matrix learning-aided Bayesian approach |
title_full_unstemmed |
Hybrid near- and far-field THz UM-MIMO channel estimation: a sparsifying matrix learning-aided Bayesian approach |
title_sort |
hybrid near- and far-field thz um-mimo channel estimation: a sparsifying matrix learning-aided bayesian approach |
publishDate |
2024 |
url |
https://hdl.handle.net/10356/181807 |
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1819113021841080320 |