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Position-aided beam selection methods have been shown to be an effectiveapproach to achieve high beamforming gain while limiting the overhead andlatency of initial access in millimeter wave (mmWave) communications. Mostresearch in the area, however, has focused on vehicular applications, where theorientation of the user terminal (UT) is mostly fixed at each position of theenvironment. This paper proposes a location- and orientation-based beamselection method to enable context information (CI)-based beam alignment inapplications where the UT can take arbitrary orientation at each location. Wepropose three different network structures, with different amounts of trainableparameters that can be used with different training dataset sizes. Aprofessional 3-dimensional ray tracing tool is used to generate datasets for anIEEE standard indoor scenario. Numerical results show the proposed networksoutperform a CI-aided benchmark such as the generalized inverse fingerprinting(GIFP) method as well as hierarchical beam search as a non-CI-based approach.Moreover, compared to the GIFP method, the proposed deep learning-based beamselection shows higher robustness to different line-of-sight blockageprobability in the training and test datasets and lower sensitivity toinaccuracies in the position and orientation information.

A Deep Learning Approach to Location- and Orientation-aided 3D Beam Selection for mmWave Communications