Location- and Orientation-Aided Millimeter Wave Beam Selection Using Deep Learning
| Autor: | Sajad Rezaie, Elisabeth de Carvalho, Carles Navarro Manchon |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Beamforming
Artificial neural network business.industry Computer science Orientation (computer vision) Deep learning 05 social sciences 050801 communication & media studies Handset law.invention 0508 media and communications Robustness (computer science) law 0502 economics and business Physics::Accelerator Physics Overhead (computing) 050211 marketing Artificial intelligence business Algorithm Beam (structure) |
| Zdroj: | ICC Rezaie, S, Manchon, C N & De Carvalho, E 2020, Location-and Orientation-Aided Millimeter Wave Beam Selection Using Deep Learning . in ICC 2020-2020 IEEE International Conference on Communications (ICC) ., 9149272, IEEE, IEEE International Conference on Communications, 2020 IEEE International Conference on Communications, ICC 2020, Dublin, Ireland, 07/06/2020 . https://doi.org/10.1109/ICC40277.2020.9149272 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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