Enhancing Beamformed Fingerprint Outdoor Positioning with Hierarchical Convolutional Neural Networks
| Autor: | Leonel Sousa, Gabriel Falcao, Joao Gante |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Beamforming
business.industry Computer science Deep learning 05 social sciences Transmitter Codebook 050801 communication & media studies 020206 networking & telecommunications 02 engineering and technology Radiation Convolutional neural network Base station 0508 media and communications 0202 electrical engineering electronic engineering information engineering Computer vision Artificial intelligence business 5G Multipath propagation |
| Zdroj: | ICASSP |
| DOI: | 10.1109/icassp.2019.8683782 |
| Popis: | With 5G millimeter wave communications, the resulting radiation reflects on most visible objects, creating rich multipath environments. The radiation is thus significantly shaped by the obstacles it interacts with, carrying latent information regarding the relative positions of the transmitter, the obstacles, and the mobile receiver. Through a pre-estabilhed codebook of beamforming patterns transmitted by a base station, the concept of beamformed fingerprints for mobile devices’ outdoor positioning has been previously proposed. In this paper, a tailored hierarchical convolutional neural network is proposed to further leverage the structure in the aforementioned hidden information. Average errors of down to 3.3 meters are obtained on a simulation environment based on realistic outdoor scenarios, containing mostly non-line-of-sight positions, making it a very competitive and promising alternative for outdoor positioning. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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