Beam Management with Orientation and RSRP using Deep Learning for Beyond
5G Systems
- URL: http://arxiv.org/abs/2202.02247v1
- Date: Fri, 4 Feb 2022 17:25:48 GMT
- Title: Beam Management with Orientation and RSRP using Deep Learning for Beyond
5G Systems
- Authors: Khuong N. Nguyen, Anum Ali, Jianhua Mo, Boon Loong Ng, Vutha Va, and
Jianzhong Charlie Zhang
- Abstract summary: We use the orientation information coming from the inertial measurement unit (IMU) for effective Beam Management (BM)
We use a data-driven strategy that fuses the reference signal received power (RSRP) with orientation information using a recurrent neural network (RNN)
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.
- Score: 6.257440291371456
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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