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Autores principales:	Gupta, Nishant, Sarajlic, Muris, Larsson, Erik G.
Formato:	Preprint
Publicado:	2025
Materias:	Signal Processing
Acceso en línea:	https://arxiv.org/abs/2507.09244
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

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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
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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

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