Name
Title | ||
|---|---|---|
Transfer Learning-Based Received Power Prediction Using RGB-D Camera in mmWave Networks |
Abstract | ||
|---|---|---|
This paper proposes a pre-training method for a deep- neural-network (DNN) based received power prediction scheme leveraging transfer learning for millimeter-wave (mmWave) networks. The received power prediction scheme has been proposed for proactive network control, which accurately predicts the received power 500 ms ahead using depth- camera images and a DNN. However, the prediction scheme requires a large number of training datasets and computational resources to prepare the accurate prediction model. In this paper, we propose a pre-training method that reduces the preparation time by leveraging the use of 3D model simulations with signal propagation simulations and transfer learning. The proposed method generates a dataset for pre- training using computer simulations, and trains a prediction model. The pre-trained model is transferred and fine-tuned by using a dataset obtained in a place where the system is actually used so that the model fits to the place. The experimental results show that the computational time of the proposed scheme with an RMS error of less than 5 dB is reduced by 78% compared with the previous work when using the dataset obtained in 60 s. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/VTCSpring.2019.8746643 | 2019 IEEE 89th Vehicular Technology Conference (VTC2019-Spring) |
Keywords | Field | DocType |
mmWave networks,DNN,millimeter-wave networks,proactive network control,depth- camera images,computational resources,signal propagation simulations,computational time,RGB-D camera,computer simulations,transfer learning-based received power prediction,deep neural-network,3D model simulations | Computer science,Transfer of learning,Electronic engineering,Real-time computing,Root-mean-square deviation,RGB color model,Train,Network control,Radio propagation | Conference |
ISSN | ISBN | Citations |
1090-3038 | 978-1-7281-1218-3 | 0 |
PageRank | References | Authors |
0.34 | 6 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tomoya Mikuma | 1 | 0 | 0.34 |
| Takayuki Nishio | 2 | 106 | 38.21 |
| Masahiro Morikura | 3 | 184 | 63.42 |
| Koji Yamamoto | 4 | 135 | 45.58 |
| yusuke asai | 5 | 0 | 1.35 |
| Ryo Miyatake | 6 | 4 | 1.42 |