Deep Learning-based Beamforming and Blockage Prediction for Sub-6GHz/mm Wave Mobile Networks
| Autor: | Megumi Kaneko, Fabian Gottsch |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Beamforming
business.industry Computer science Deep learning Real-time computing 020206 networking & telecommunications 020302 automobile design & engineering 02 engineering and technology Base station 0203 mechanical engineering Robustness (computer science) 0202 electrical engineering electronic engineering information engineering Artificial intelligence business 5G Communication channel |
| Zdroj: | GLOBECOM |
| DOI: | 10.1109/globecom42002.2020.9322404 |
| Popis: | To meet the stringent demands of Beyond 5G applications, an optimized and seamless usage of sub-6 GHz and mmWave networks under high user mobility is essential. In particular, to alleviate the heavy burdens of mmWave channel state information feedback, we propose a Deep Learning-based scheduler at the base station that predicts the future mmWave blockage status and optimal beamforming vectors of the mobile user, solely based on sub-6 GHz channel knowledge, i.e., out-of-band information. The designed Deep Neural Network (DNN) comprises Long Short-Term Memory layers to extract the temporal correlation from the low-frequency channel knowledge, for enabling an accurate blockage and beam prediction. We investigate the influence of the available past channel information and of the user speed on the prediction accuracy and on the achievable data rates. Simulation results show that the proposed method significantly improves the prediction accuracy of optimal mmWave beamformers compared to the benchmark DNN with much reduced complexity. Furthermore, the proposed DNN demonstrates high robustness for different user speeds, while approaching optimal rates achieved through exhaustive search and perfect blockage knowledge. |
| Databáze: | OpenAIRE |
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