Deep Compressed Sensing for Terahertz Ultra-Massive MIMO Channel Estimation
Envisioned as a pivotal technology for sixth-generation (6G) and beyond, Terahertz (THz) band communications can potentially satisfy the increasing demand for ultra-high-speed wireless links. While ultra-massive multiple-input multiple-output (UM-MIMO) is promising in counteracting the exceptionally...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Open Journal of the Communications Society |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10899780/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849328080146923520 |
|---|---|
| author | Ganghui Lin Mikail Erdem Mohamed-Slim Alouini |
| author_facet | Ganghui Lin Mikail Erdem Mohamed-Slim Alouini |
| author_sort | Ganghui Lin |
| collection | DOAJ |
| description | Envisioned as a pivotal technology for sixth-generation (6G) and beyond, Terahertz (THz) band communications can potentially satisfy the increasing demand for ultra-high-speed wireless links. While ultra-massive multiple-input multiple-output (UM-MIMO) is promising in counteracting the exceptionally high path loss at THz frequency, the channel estimation (CE) of this extensive antenna system introduces significant challenges. In this paper, we propose a deep compressed sensing (DCS) framework based on generative neural networks for THz CE. The proposed estimator generates realistic THz channel samples to avoid complex channel modeling for THz UM-MIMO systems, especially in the near field. More importantly, the estimator is optimized for fast channel inference. Our results show significant superiority over the baseline generative adversarial network (GAN) estimator and traditional estimators. Compared to conventional estimators, our model achieves at least 8 dB lower normalized mean squared error (NMSE). Against GAN estimator, our model achieves around 3 dB lower NMSE at 0 dB SNR with one order of magnitude lower computation complexity. Moreover, our model achieves lower training overhead compared to GAN with empirically 4 times faster training convergence. |
| format | Article |
| id | doaj-art-a2eb8a13f6354cfe97f3ea7abc841bca |
| institution | Kabale University |
| issn | 2644-125X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of the Communications Society |
| spelling | doaj-art-a2eb8a13f6354cfe97f3ea7abc841bca2025-08-20T03:47:41ZengIEEEIEEE Open Journal of the Communications Society2644-125X2025-01-0161747176210.1109/OJCOMS.2025.354487110899780Deep Compressed Sensing for Terahertz Ultra-Massive MIMO Channel EstimationGanghui Lin0https://orcid.org/0000-0002-3436-3737Mikail Erdem1https://orcid.org/0000-0003-3501-4229Mohamed-Slim Alouini2https://orcid.org/0000-0003-4827-1793Division of Computer, Electrical and Mathematical Sciences and Engineering, King Abdullah University of Science and Technology, Thuwal, Saudi ArabiaDivision of Computer, Electrical and Mathematical Sciences and Engineering, King Abdullah University of Science and Technology, Thuwal, Saudi ArabiaDivision of Computer, Electrical and Mathematical Sciences and Engineering, King Abdullah University of Science and Technology, Thuwal, Saudi ArabiaEnvisioned as a pivotal technology for sixth-generation (6G) and beyond, Terahertz (THz) band communications can potentially satisfy the increasing demand for ultra-high-speed wireless links. While ultra-massive multiple-input multiple-output (UM-MIMO) is promising in counteracting the exceptionally high path loss at THz frequency, the channel estimation (CE) of this extensive antenna system introduces significant challenges. In this paper, we propose a deep compressed sensing (DCS) framework based on generative neural networks for THz CE. The proposed estimator generates realistic THz channel samples to avoid complex channel modeling for THz UM-MIMO systems, especially in the near field. More importantly, the estimator is optimized for fast channel inference. Our results show significant superiority over the baseline generative adversarial network (GAN) estimator and traditional estimators. Compared to conventional estimators, our model achieves at least 8 dB lower normalized mean squared error (NMSE). Against GAN estimator, our model achieves around 3 dB lower NMSE at 0 dB SNR with one order of magnitude lower computation complexity. Moreover, our model achieves lower training overhead compared to GAN with empirically 4 times faster training convergence.https://ieeexplore.ieee.org/document/10899780/Channel estimationgenerative neural networkTerahertzultra-massive MIMO |
| spellingShingle | Ganghui Lin Mikail Erdem Mohamed-Slim Alouini Deep Compressed Sensing for Terahertz Ultra-Massive MIMO Channel Estimation IEEE Open Journal of the Communications Society Channel estimation generative neural network Terahertz ultra-massive MIMO |
| title | Deep Compressed Sensing for Terahertz Ultra-Massive MIMO Channel Estimation |
| title_full | Deep Compressed Sensing for Terahertz Ultra-Massive MIMO Channel Estimation |
| title_fullStr | Deep Compressed Sensing for Terahertz Ultra-Massive MIMO Channel Estimation |
| title_full_unstemmed | Deep Compressed Sensing for Terahertz Ultra-Massive MIMO Channel Estimation |
| title_short | Deep Compressed Sensing for Terahertz Ultra-Massive MIMO Channel Estimation |
| title_sort | deep compressed sensing for terahertz ultra massive mimo channel estimation |
| topic | Channel estimation generative neural network Terahertz ultra-massive MIMO |
| url | https://ieeexplore.ieee.org/document/10899780/ |
| work_keys_str_mv | AT ganghuilin deepcompressedsensingforterahertzultramassivemimochannelestimation AT mikailerdem deepcompressedsensingforterahertzultramassivemimochannelestimation AT mohamedslimalouini deepcompressedsensingforterahertzultramassivemimochannelestimation |