Channel Estimation for Wideband Multi-RIS-Assisted mmWave Massive-MIMO OFDM System With Beam Squint Effect
Reconfigurable intelligent surfaces (RISs) and massive multiple-input multiple-output (massive-MIMO) systems are promising technologies for improving the energy efficiency of millimeter-wave (mmWave) communication. Furthermore, in urban areas, where there is high obscurity, multiple RISs can be depl...
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/11021458/ |
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| author | Thabang C. Rapudu Olutayo O. Oyerinde |
| author_facet | Thabang C. Rapudu Olutayo O. Oyerinde |
| author_sort | Thabang C. Rapudu |
| collection | DOAJ |
| description | Reconfigurable intelligent surfaces (RISs) and massive multiple-input multiple-output (massive-MIMO) systems are promising technologies for improving the energy efficiency of millimeter-wave (mmWave) communication. Furthermore, in urban areas, where there is high obscurity, multiple RISs can be deployed to circumvent blockages between communicating nodes. However, deploying both multi-RIS and massive-MIMO systems significantly increases the dimensionality of a wireless communication channel and thus, accurate channel state information (CSI) acquisition by channel estimation (CE) becomes non-trivial mainly due to the passive nature of the RISs. Additionally, existing wideband RIS-assisted CE schemes ignore the beam squint effect despite its severe CE performance degradation. Therefore, in this paper, a beam squint aware machine learning (ML)-based uplink CE scheme for wideband multi-RIS-assisted mmWave massive-MIMO orthogonal frequency division multiplexing (OFDM) system is proposed. Specifically, to reduce the beam squint effect, the bandwidth of the system is divided into subbands, and thereafter, a denoising convolutional neural network bidirectional long-short term memory (DnCNN-Bi-LSTM) scheme is proposed for cascaded uplink CE. For certain parameter settings, the proposed beam squint aware DnCNN-Bi-LSTM CE scheme achieves better normalized minimum mean squared error (NMSE) performance than the state-of-the-art beam squint aware CE methods. |
| format | Article |
| id | doaj-art-e16af6d4e0354debb2d4b69695e029df |
| institution | OA Journals |
| issn | 2644-125X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| series | IEEE Open Journal of the Communications Society |
| spelling | doaj-art-e16af6d4e0354debb2d4b69695e029df2025-08-20T02:07:10ZengIEEEIEEE Open Journal of the Communications Society2644-125X2025-01-0164804481710.1109/OJCOMS.2025.357594711021458Channel Estimation for Wideband Multi-RIS-Assisted mmWave Massive-MIMO OFDM System With Beam Squint EffectThabang C. Rapudu0https://orcid.org/0000-0002-0493-0083Olutayo O. Oyerinde1https://orcid.org/0000-0002-7827-5448School of Electrical and Information Engineering, The University of the Witwatersrand, Johannesburg, South AfricaSchool of Electrical and Information Engineering, The University of the Witwatersrand, Johannesburg, South AfricaReconfigurable intelligent surfaces (RISs) and massive multiple-input multiple-output (massive-MIMO) systems are promising technologies for improving the energy efficiency of millimeter-wave (mmWave) communication. Furthermore, in urban areas, where there is high obscurity, multiple RISs can be deployed to circumvent blockages between communicating nodes. However, deploying both multi-RIS and massive-MIMO systems significantly increases the dimensionality of a wireless communication channel and thus, accurate channel state information (CSI) acquisition by channel estimation (CE) becomes non-trivial mainly due to the passive nature of the RISs. Additionally, existing wideband RIS-assisted CE schemes ignore the beam squint effect despite its severe CE performance degradation. Therefore, in this paper, a beam squint aware machine learning (ML)-based uplink CE scheme for wideband multi-RIS-assisted mmWave massive-MIMO orthogonal frequency division multiplexing (OFDM) system is proposed. Specifically, to reduce the beam squint effect, the bandwidth of the system is divided into subbands, and thereafter, a denoising convolutional neural network bidirectional long-short term memory (DnCNN-Bi-LSTM) scheme is proposed for cascaded uplink CE. For certain parameter settings, the proposed beam squint aware DnCNN-Bi-LSTM CE scheme achieves better normalized minimum mean squared error (NMSE) performance than the state-of-the-art beam squint aware CE methods.https://ieeexplore.ieee.org/document/11021458/Channel estimationreconfigurable intelligent surfacebeam squintmachine learningMIMO |
| spellingShingle | Thabang C. Rapudu Olutayo O. Oyerinde Channel Estimation for Wideband Multi-RIS-Assisted mmWave Massive-MIMO OFDM System With Beam Squint Effect IEEE Open Journal of the Communications Society Channel estimation reconfigurable intelligent surface beam squint machine learning MIMO |
| title | Channel Estimation for Wideband Multi-RIS-Assisted mmWave Massive-MIMO OFDM System With Beam Squint Effect |
| title_full | Channel Estimation for Wideband Multi-RIS-Assisted mmWave Massive-MIMO OFDM System With Beam Squint Effect |
| title_fullStr | Channel Estimation for Wideband Multi-RIS-Assisted mmWave Massive-MIMO OFDM System With Beam Squint Effect |
| title_full_unstemmed | Channel Estimation for Wideband Multi-RIS-Assisted mmWave Massive-MIMO OFDM System With Beam Squint Effect |
| title_short | Channel Estimation for Wideband Multi-RIS-Assisted mmWave Massive-MIMO OFDM System With Beam Squint Effect |
| title_sort | channel estimation for wideband multi ris assisted mmwave massive mimo ofdm system with beam squint effect |
| topic | Channel estimation reconfigurable intelligent surface beam squint machine learning MIMO |
| url | https://ieeexplore.ieee.org/document/11021458/ |
| work_keys_str_mv | AT thabangcrapudu channelestimationforwidebandmultirisassistedmmwavemassivemimoofdmsystemwithbeamsquinteffect AT olutayoooyerinde channelestimationforwidebandmultirisassistedmmwavemassivemimoofdmsystemwithbeamsquinteffect |