Location- and Orientation-Aided Millimeter Wave Beam Selection Using Deep Learning

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Location-aided beam alignment methods exploit the user location and prior knowledge of the propagation environment to identify the beam directions that are more likely to maximize the beamforming gain, allowing for a reduction of the beam training overhead. They have been especially popular for vehicle to everything (V2X) applications where the receive array orientation is approximately constant for each considered location, but are not directly applicable to pedestrian applications with arbitrary orientation of the user handset. This paper proposes a deep neural network based beam selection method that leverages position and orientation of the receiver to recommend a shortlist of the best beam pairs, thus significantly reducing the alignment overhead. Moreover, we use multi-labeled classification to not only capture the beam pair with highest received strength but also enrich the neural network with information of alternative beam pairs with high received signal strength, providing robustness against blockage. Simulation results show the better performance of the proposed method compared to a generalization of the inverse fingerprinting algorithm in terms of the misalignment and outage probabilities.
Original languageEnglish
Title of host publicationICC 2020 - 2020 IEEE International Conference on Communications (ICC)
Number of pages6
Publication date27 Jul 2020
Article number9149272
ISBN (Print)978-1-7281-5090-1
ISBN (Electronic)978-1-7281-5089-5
Publication statusPublished - 27 Jul 2020
Event2020 IEEE International Conference on Communications, ICC 2020 - Dublin, Ireland
Duration: 7 Jun 202011 Jun 2020


Conference2020 IEEE International Conference on Communications, ICC 2020
Internet address
SeriesIEEE International Conference on Communications


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