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Beam Management with Orientation and RSRP using Deep Learning for Beyond 5G Systems

2022-02-04 17:25:48
Khuong N. Nguyen, Anum Ali, Jianhua Mo, Boon Loong Ng, Vutha Va, Jianzhong Charlie Zhang
arXiv_AI
         
Abstract

Beam management (BM), i.e., the process of finding and maintaining a suitable transmit and receive beam pair, can be challenging, particularly in highly dynamic scenarios. Side-information, e.g., orientation, from on-board sensors can assist the user equipment (UE) BM. In this work, we use the orientation information coming from the inertial measurement unit (IMU) for effective BM. We use a data-driven strategy that fuses the reference signal received power (RSRP) with orientation information using a recurrent neural network (RNN). Simulation results show that the proposed strategy performs much better than the conventional BM and an orientation-assisted BM strategy that utilizes particle filter in another study. Specifically, the proposed data-driven strategy improves the beam-prediction accuracy up to 34% and increases mean RSRP by up to 4.2 dB when the UE orientation changes quickly.

Abstract (translated)

URL

https://arxiv.org/abs/2202.02247

PDF

https://arxiv.org/pdf/2202.02247.pdf

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