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Beam management (BM), i.e., the process of finding and maintaining a suitabletransmit and receive beam pair, can be challenging, particularly in highlydynamic scenarios. Side-information, e.g., orientation, from on-board sensorscan assist the user equipment (UE) BM. In this work, we use the orientationinformation 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 thanthe conventional BM and an orientation-assisted BM strategy that utilizesparticle filter in another study. Specifically, the proposed data-drivenstrategy improves the beam-prediction accuracy up to 34% and increases meanRSRP by up to 4.2 dB when the UE orientation changes quickly.

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