Name
Title | ||
|---|---|---|
Impact of Input Data Size on Received Power Prediction Using Depth Images for mm Wave Communications. |
Abstract | ||
|---|---|---|
This paper experimentally finds the optimum number of input images of a machine learning-based mmWave received signal strength (RSS) value prediction scheme from depth images. By modeling the relationships between time-sequential depth images and RSS values based on machine learning, it is possible to predict the future RSS values, and thereby, a predictive handover makes a moment of degradation of the RSS value avoidable. As prediction models of RSS value, three machine learning models are compared: the convolutional neural networks (CNN), the combination of CNN and convolutional long short-term memory (CNN+ConvLSTM), and random forest. As the number of input images increases, the prediction accuracy generally improves, however, too numerous input images may make the prediction accuracy worse because of over-fitting. Experimental results reveal that the number of input images that are input in order to predict the RSS value the most accurately is 16. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/VTCFall.2018.8690739 | VTC-Fall |
Field | DocType | Citations |
Pattern recognition,Convolutional neural network,Computer science,Electronic engineering,Artificial intelligence,Signal strength,Predictive modelling,Random forest,RSS,Handover | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kota Nakashima | 1 | 8 | 2.49 |
| Yusuke Koda | 2 | 4 | 3.78 |
| Koji Yamamoto | 3 | 135 | 45.58 |
| Hironao Okamoto | 4 | 6 | 1.47 |
| Takayuki Nishio | 5 | 106 | 38.21 |
| Masahiro Morikura | 6 | 184 | 63.42 |
| yusuke asai | 7 | 0 | 1.35 |
| Ryo Miyatake | 8 | 4 | 1.42 |