Time-Domain Channel Estimation for Extremely Large MIMO THz Communication Systems Under Dual-Wideband Fading Conditions
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866913420023955456 |
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| author | Vlachos, Evangelos Kaushik, Aryan Eldar, Yonina C. Alexandropoulos, George C. |
| author_facet | Vlachos, Evangelos Kaushik, Aryan Eldar, Yonina C. Alexandropoulos, George C. |
| contents | In this paper, we study the problem of extremely large (XL) multiple-input multiple-output (MIMO) channel estimation in the terahertz (THz) frequency band, considering the presence of propagation delays across the entire array apertures at both communication ends, which naturally leads to frequency selectivity. This problem is known as beam squint and may be pronounced when communications are subject to multipath fading conditions. Multi-carrier (MC) transmission schemes, which are usually deployed in THz communication systems to address these issues, suffer from high peak-to-average power ratio, which is specifically dominant in this frequency band where low transmit power is mostly feasible. Furthermore, the frequency selectivity caused by severe molecular absorption in the THz band necessitates delicate consideration in MC system design. Motivated by the benefits of single-carrier (SC) waveforms for practical THz communication systems, diverging from the current dominant research trend on MC systems, we devise a novel channel estimation problem formulation in the time domain for SC XL MIMO systems subject to multipath signal propagation, spatial wideband effects, and molecular absorption. An efficient alternating minimization approach is presented to solve the proposed mixed-integer sparse problem formulation. The conducted extensive performance evaluation results validate that the proposed XL MIMO estimation scheme exhibits superior performance than conventional SC- and MC-based techniques, approaching the idealized lower bound. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_14745 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Time-Domain Channel Estimation for Extremely Large MIMO THz Communication Systems Under Dual-Wideband Fading Conditions Vlachos, Evangelos Kaushik, Aryan Eldar, Yonina C. Alexandropoulos, George C. Information Theory Signal Processing In this paper, we study the problem of extremely large (XL) multiple-input multiple-output (MIMO) channel estimation in the terahertz (THz) frequency band, considering the presence of propagation delays across the entire array apertures at both communication ends, which naturally leads to frequency selectivity. This problem is known as beam squint and may be pronounced when communications are subject to multipath fading conditions. Multi-carrier (MC) transmission schemes, which are usually deployed in THz communication systems to address these issues, suffer from high peak-to-average power ratio, which is specifically dominant in this frequency band where low transmit power is mostly feasible. Furthermore, the frequency selectivity caused by severe molecular absorption in the THz band necessitates delicate consideration in MC system design. Motivated by the benefits of single-carrier (SC) waveforms for practical THz communication systems, diverging from the current dominant research trend on MC systems, we devise a novel channel estimation problem formulation in the time domain for SC XL MIMO systems subject to multipath signal propagation, spatial wideband effects, and molecular absorption. An efficient alternating minimization approach is presented to solve the proposed mixed-integer sparse problem formulation. The conducted extensive performance evaluation results validate that the proposed XL MIMO estimation scheme exhibits superior performance than conventional SC- and MC-based techniques, approaching the idealized lower bound. |
| title | Time-Domain Channel Estimation for Extremely Large MIMO THz Communication Systems Under Dual-Wideband Fading Conditions |
| topic | Information Theory Signal Processing |
| url | https://arxiv.org/abs/2310.14745 |