Beam Management with Orientation and RSRP using Deep Learning for Beyond 5G Systems
This addresses beam management challenges for user equipment in highly dynamic scenarios, representing an incremental improvement over existing orientation-assisted methods.
The paper tackles beam management in dynamic scenarios for beyond 5G systems by fusing orientation data from IMU sensors with RSRP using a recurrent neural network, resulting in up to 34% improvement in beam-prediction accuracy and up to 4.2 dB increase in mean RSRP compared to conventional methods.
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.