Terahertz-Band Channel and Beam Split Estimation via Array Perturbation Model
For the demonstration of ultra-wideband bandwidth and pencil-beamforming, the terahertz (THz)-band has been envisioned as one of the key enabling technologies for the sixth generation networks. However, the acquisition of the THz channel entails several unique challenges such as severe path loss and...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Open Journal of the Communications Society |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10089857/ |
| _version_ | 1797849295596027904 |
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| author | Ahmet M. Elbir Wei Shi Anastasios K. Papazafeiropoulos Pandelis Kourtessis Symeon Chatzinotas |
| author_facet | Ahmet M. Elbir Wei Shi Anastasios K. Papazafeiropoulos Pandelis Kourtessis Symeon Chatzinotas |
| author_sort | Ahmet M. Elbir |
| collection | DOAJ |
| description | For the demonstration of ultra-wideband bandwidth and pencil-beamforming, the terahertz (THz)-band has been envisioned as one of the key enabling technologies for the sixth generation networks. However, the acquisition of the THz channel entails several unique challenges such as severe path loss and beam-split. Prior works usually employ ultra-massive arrays and additional hardware components comprised of time-delayers to compensate for these loses. In order to provide a cost-effective solution, this paper introduces a sparse-Bayesian-learning (SBL) technique for joint channel and beam-split estimation. Specifically, we first model the beam-split as an array perturbation inspired from array signal processing. Next, a low-complexity approach is developed by exploiting the line-of-sight-dominant feature of THz channel to reduce the computational complexity involved in the proposed SBL technique for channel estimation (SBCE). Additionally, based on federated-learning, we implement a model-free technique to the proposed model-based SBCE solution. Further to that, we examine the near-field considerations of THz channel, and introduce the range-dependent near-field beam-split. The theoretical performance bounds, i.e., Cramér-Rao lower bounds, are derived both for near- and far-field parameters, e.g., user directions, beam-split and ranges. Numerical simulations demonstrate that SBCE outperforms the existing approaches and exhibits lower hardware cost. |
| first_indexed | 2024-04-09T18:41:33Z |
| format | Article |
| id | doaj.art-41b1493e95c5436883e55dfe007819ae |
| institution | Directory Open Access Journal |
| issn | 2644-125X |
| language | English |
| last_indexed | 2024-04-09T18:41:33Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of the Communications Society |
| spelling | doaj.art-41b1493e95c5436883e55dfe007819ae2023-04-10T23:01:47ZengIEEEIEEE Open Journal of the Communications Society2644-125X2023-01-01489290710.1109/OJCOMS.2023.326362510089857Terahertz-Band Channel and Beam Split Estimation via Array Perturbation ModelAhmet M. Elbir0https://orcid.org/0000-0003-4060-3781Wei Shi1https://orcid.org/0000-0002-3071-8350Anastasios K. Papazafeiropoulos2https://orcid.org/0000-0003-1841-6461Pandelis Kourtessis3https://orcid.org/0000-0003-3392-670XSymeon Chatzinotas4https://orcid.org/0000-0001-5122-0001Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg, Esch-sur-Alzette, LuxembourgSchool of Information Technology, Carleton University, Ottawa, ON, CanadaCommunications and Intelligent Systems Research Group, University of Hertfordshire, Hatfield, U.KCommunications and Intelligent Systems Research Group, University of Hertfordshire, Hatfield, U.KInterdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg, Esch-sur-Alzette, LuxembourgFor the demonstration of ultra-wideband bandwidth and pencil-beamforming, the terahertz (THz)-band has been envisioned as one of the key enabling technologies for the sixth generation networks. However, the acquisition of the THz channel entails several unique challenges such as severe path loss and beam-split. Prior works usually employ ultra-massive arrays and additional hardware components comprised of time-delayers to compensate for these loses. In order to provide a cost-effective solution, this paper introduces a sparse-Bayesian-learning (SBL) technique for joint channel and beam-split estimation. Specifically, we first model the beam-split as an array perturbation inspired from array signal processing. Next, a low-complexity approach is developed by exploiting the line-of-sight-dominant feature of THz channel to reduce the computational complexity involved in the proposed SBL technique for channel estimation (SBCE). Additionally, based on federated-learning, we implement a model-free technique to the proposed model-based SBCE solution. Further to that, we examine the near-field considerations of THz channel, and introduce the range-dependent near-field beam-split. The theoretical performance bounds, i.e., Cramér-Rao lower bounds, are derived both for near- and far-field parameters, e.g., user directions, beam-split and ranges. Numerical simulations demonstrate that SBCE outperforms the existing approaches and exhibits lower hardware cost.https://ieeexplore.ieee.org/document/10089857/Terahertzchannel estimationbeam splitsparse Bayesian learningnear-fieldfederated learning |
| spellingShingle | Ahmet M. Elbir Wei Shi Anastasios K. Papazafeiropoulos Pandelis Kourtessis Symeon Chatzinotas Terahertz-Band Channel and Beam Split Estimation via Array Perturbation Model IEEE Open Journal of the Communications Society Terahertz channel estimation beam split sparse Bayesian learning near-field federated learning |
| title | Terahertz-Band Channel and Beam Split Estimation via Array Perturbation Model |
| title_full | Terahertz-Band Channel and Beam Split Estimation via Array Perturbation Model |
| title_fullStr | Terahertz-Band Channel and Beam Split Estimation via Array Perturbation Model |
| title_full_unstemmed | Terahertz-Band Channel and Beam Split Estimation via Array Perturbation Model |
| title_short | Terahertz-Band Channel and Beam Split Estimation via Array Perturbation Model |
| title_sort | terahertz band channel and beam split estimation via array perturbation model |
| topic | Terahertz channel estimation beam split sparse Bayesian learning near-field federated learning |
| url | https://ieeexplore.ieee.org/document/10089857/ |
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