Name
Abstract | ||
|---|---|---|
In this letter we present our results on how deep learning can be leveraged for outdoor path loss prediction in the 30GHz band. In particular, we exploit deep learning to boost the performance of outdoor path loss prediction in an end-to-end manner. In contrast to existing 3D ray tracing approaches that use geometrical information to model physical radio propagation phenomena, the proposed deep learning-based approach predicts outdoor path loss in the urban 5G scenario directly. To achieve this, a deep learning model is first trained offline using the data generated from simulations utilizing a 3D ray tracing approach. Our simulation results have revealed that the deep learning based approach can deliver outdoor path loss prediction in the 5G scenario with a performance comparable to a state-of-the-art 3D ray tracing simulator. Furthermore, the deep learning-based approach is 30 times faster than the ray tracing approach. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/LWC.2022.3175091 | IEEE Wireless Communications Letters |
Keywords | DocType | Volume |
Ray tracing,radio propagation,deep learning,convolutional neural network,5G | Journal | 11 |
Issue | ISSN | Citations |
8 | 2162-2337 | 0 |
PageRank | References | Authors |
0.34 | 5 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kehai Qiu | 1 | 0 | 0.34 |
| Stefanos Bakirtzis | 2 | 0 | 0.34 |
| Hui Song | 3 | 0 | 0.34 |
| Jie Zhang | 4 | 814 | 68.89 |
| Ian J. Wassell | 5 | 288 | 35.10 |