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Abstract
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 language | English |
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Title of host publication | ICC 2020 - 2020 IEEE International Conference on Communications (ICC) |
Number of pages | 6 |
Publisher | IEEE |
Publication date | 27 Jul 2020 |
Article number | 9149272 |
ISBN (Print) | 978-1-7281-5090-1 |
ISBN (Electronic) | 978-1-7281-5089-5 |
DOIs | |
Publication status | Published - 27 Jul 2020 |
Event | 2020 IEEE International Conference on Communications, ICC 2020 - Dublin, Ireland Duration: 7 Jun 2020 → 11 Jun 2020 https://icc2020.ieee-icc.org/ |
Conference
Conference | 2020 IEEE International Conference on Communications, ICC 2020 |
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Country/Territory | Ireland |
City | Dublin |
Period | 07/06/2020 → 11/06/2020 |
Internet address |
Series | IEEE International Conference on Communications |
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ISSN | 1938-1883 |
Fingerprint
Dive into the research topics of 'Location- and Orientation-Aided Millimeter Wave Beam Selection Using Deep Learning'. Together they form a unique fingerprint.Projects
- 1 Finished
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Leveraging Context Information in Fifth Generation MillimeterWave Mobile Networks: a Bayesian Approach
01/03/2019 → 31/08/2022
Project: Research