Near-Field Terahertz Communications: Model-Based and Model-Free Channel Estimation
Terahertz (THz) band is expected to be one of the key enabling technologies of the sixth generation (6G) wireless networks because of its abundant available bandwidth and very narrow beamwidth. Due to high frequency operations, electrically small array apertures are employed, and the signal wavefron...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10098795/ |
| _version_ | 1827965567340904448 |
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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 | Terahertz (THz) band is expected to be one of the key enabling technologies of the sixth generation (6G) wireless networks because of its abundant available bandwidth and very narrow beamwidth. Due to high frequency operations, electrically small array apertures are employed, and the signal wavefront becomes spherical in the near-field. Therefore, near-field signal model should be considered for channel acquisition in THz systems. Unlike prior works which mostly ignore the impact of near-field beam-squint (NB) and consider either narrowband scenario or far-field models, this paper introduces both a model-based and a model-free techniques for wideband THz channel estimation in the presence of NB. The model-based approach is based on orthogonal matching pursuit (OMP) algorithm, for which we design an NB-aware dictionary. The key idea is to exploit the angular and range deviations due to the NB. We then employ the OMP algorithm, which accounts for the deviations thereby ipso facto mitigating the effect of NB. We further introduce a federated learning (FL)-based approach as a model-free solution for channel estimation in a multi-user scenario to achieve reduced complexity and training overhead. Through numerical simulations, we demonstrate the effectiveness of the proposed channel estimation techniques for wideband THz systems in comparison with the existing state-of-the-art techniques. |
| first_indexed | 2024-04-09T17:36:36Z |
| format | Article |
| id | doaj.art-ba68968765a64a3c886272de1264f710 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-09T17:36:36Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-ba68968765a64a3c886272de1264f7102023-04-17T23:00:19ZengIEEEIEEE Access2169-35362023-01-0111364093642010.1109/ACCESS.2023.326629710098795Near-Field Terahertz Communications: Model-Based and Model-Free Channel EstimationAhmet 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, CanadaInterdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg, Esch-sur-Alzette, LuxembourgCommunications and Intelligent Systems Research Group, University of Hertfordshire, Hatfield, U.KInterdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg, Esch-sur-Alzette, LuxembourgTerahertz (THz) band is expected to be one of the key enabling technologies of the sixth generation (6G) wireless networks because of its abundant available bandwidth and very narrow beamwidth. Due to high frequency operations, electrically small array apertures are employed, and the signal wavefront becomes spherical in the near-field. Therefore, near-field signal model should be considered for channel acquisition in THz systems. Unlike prior works which mostly ignore the impact of near-field beam-squint (NB) and consider either narrowband scenario or far-field models, this paper introduces both a model-based and a model-free techniques for wideband THz channel estimation in the presence of NB. The model-based approach is based on orthogonal matching pursuit (OMP) algorithm, for which we design an NB-aware dictionary. The key idea is to exploit the angular and range deviations due to the NB. We then employ the OMP algorithm, which accounts for the deviations thereby ipso facto mitigating the effect of NB. We further introduce a federated learning (FL)-based approach as a model-free solution for channel estimation in a multi-user scenario to achieve reduced complexity and training overhead. Through numerical simulations, we demonstrate the effectiveness of the proposed channel estimation techniques for wideband THz systems in comparison with the existing state-of-the-art techniques.https://ieeexplore.ieee.org/document/10098795/Beamsquintchannel estimationfederated learningmachine learningnear-fieldorthogonal matching pursuit |
| spellingShingle | Ahmet M. Elbir Wei Shi Anastasios K. Papazafeiropoulos Pandelis Kourtessis Symeon Chatzinotas Near-Field Terahertz Communications: Model-Based and Model-Free Channel Estimation IEEE Access Beamsquint channel estimation federated learning machine learning near-field orthogonal matching pursuit |
| title | Near-Field Terahertz Communications: Model-Based and Model-Free Channel Estimation |
| title_full | Near-Field Terahertz Communications: Model-Based and Model-Free Channel Estimation |
| title_fullStr | Near-Field Terahertz Communications: Model-Based and Model-Free Channel Estimation |
| title_full_unstemmed | Near-Field Terahertz Communications: Model-Based and Model-Free Channel Estimation |
| title_short | Near-Field Terahertz Communications: Model-Based and Model-Free Channel Estimation |
| title_sort | near field terahertz communications model based and model free channel estimation |
| topic | Beamsquint channel estimation federated learning machine learning near-field orthogonal matching pursuit |
| url | https://ieeexplore.ieee.org/document/10098795/ |
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