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

TL;DR

This paper proposes a deep learning-based beam management strategy for beyond 5G systems that fuses orientation data from IMU sensors with RSRP measurements, significantly improving beam prediction accuracy in dynamic scenarios.

Contribution

It introduces a data-driven RNN approach that effectively combines orientation and RSRP data for beam management, outperforming conventional and particle filter-based methods.

Findings

Beam prediction accuracy improved by up to 34%.

Mean RSRP increased by up to 4.2 dB.

Outperforms existing orientation-assisted strategies.

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.