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Abstract
The main bottleneck for using deep neural networks in location-aided millimeter wave beam alignment procedures is the need for large datasets to tune their large set of trainable parameters. This letter proposes to use the transfer learning technique in order to reduce the dataset size requirements in deep-learning based beam selection. Information transfer can be done from one environment to another, or from one antenna configuration to another, which we refer to as domain and task adaptation, respectively. Numerical evaluations show a significant gain in using transfer learning in both domain and task adaptation scenarios, especially with limited datasets.
Original language | English |
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Article number | 9461767 |
Journal | I E E E Communications Letters |
Volume | 25 |
Issue number | 9 |
Pages (from-to) | 2963-2967 |
Number of pages | 5 |
ISSN | 1089-7798 |
DOIs | |
Publication status | Published - 21 Jun 2021 |
Keywords
- millimeter wave (mm-wave)
- beam alignment
- deep learning
- transfer learning
- domain adaptation
- task adaptation
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Dive into the research topics of 'Deep Transfer Learning for Location-aware Millimeter Wave Beam Selection'. 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