Deep Learning for sub-THz Radio Unit Selection using sub-10 GHz Channel Information and Inferred Device Beamforming

TL;DR

This paper proposes a deep learning-based method to select sub-THz radio units using sub-10 GHz channel data and inferred device beamforming, reducing overhead and power consumption in 6G networks.

Contribution

It introduces a novel approach to infer sub-THz RU candidates from sub-10 GHz information without requiring UE orientation data, considering inter-band beam configuration.

Findings

Deep learning accurately infers sub-THz RU candidates from sub-10 GHz channels.

Neglecting UE orientation significantly degrades system performance.

The method reduces beam search overhead and power consumption.

Abstract

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.