Deep Learning for sub-THz Radio Unit Selection using sub-10 GHz Channel Information and Inferred Device Beamforming
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866911052797575168 |
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| author | Gupta, Nishant Sarajlic, Muris Larsson, Erik G. |
| author_facet | Gupta, Nishant Sarajlic, Muris Larsson, Erik G. |
| contents | The dense and distributed deployment of sub-THz radio units (RUs) alongside sub-10 GHz access point (AP) is a promising approach to provide high data rate and reliable coverage for future 6G applications. However, beam search or RU selection for the sub-THz RUs incurs significant overhead and high power consumption. To address this, we introduce a method that leverages deep learning to infer a suitable sub-THz RU candidate from a set of sub-THz RUs using the sub-10 GHz channel characteristics. A novel aspect of this work is the consideration of inter-band beam configuration (IBBC), defined as the broadside angle between the low-band and high-band antenna patterns of the user equipment (UE). Since IBBC indicates the beamforming information or UE's orientation, it is typically not shared with the network as a part of signalling. Therefore, we propose a solution strategy to infer a suitable sub-THz RU even when UEs do not share their IBBC information. Simulation results illustrate the performance of the inferred sub-THz RU and highlights the detrimental impact of neglecting UE orientation on the systems performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_09244 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Deep Learning for sub-THz Radio Unit Selection using sub-10 GHz Channel Information and Inferred Device Beamforming Gupta, Nishant Sarajlic, Muris Larsson, Erik G. Signal Processing The dense and distributed deployment of sub-THz radio units (RUs) alongside sub-10 GHz access point (AP) is a promising approach to provide high data rate and reliable coverage for future 6G applications. However, beam search or RU selection for the sub-THz RUs incurs significant overhead and high power consumption. To address this, we introduce a method that leverages deep learning to infer a suitable sub-THz RU candidate from a set of sub-THz RUs using the sub-10 GHz channel characteristics. A novel aspect of this work is the consideration of inter-band beam configuration (IBBC), defined as the broadside angle between the low-band and high-band antenna patterns of the user equipment (UE). Since IBBC indicates the beamforming information or UE's orientation, it is typically not shared with the network as a part of signalling. Therefore, we propose a solution strategy to infer a suitable sub-THz RU even when UEs do not share their IBBC information. Simulation results illustrate the performance of the inferred sub-THz RU and highlights the detrimental impact of neglecting UE orientation on the systems performance. |
| title | Deep Learning for sub-THz Radio Unit Selection using sub-10 GHz Channel Information and Inferred Device Beamforming |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2507.09244 |