Name
Title | ||
|---|---|---|
Deep Channel Learning for Large Intelligent Surfaces Aided mm-Wave Massive MIMO Systems |
Abstract | ||
|---|---|---|
This letter presents the first work introducing a deep learning (DL) framework for channel estimation in large intelligent surface (LIS) assisted massive MIMO (multiple-input multiple-output) systems. A twin convolutional neural network (CNN) architecture is designed and it is fed with the received pilot signals to estimate both direct and cascaded channels. In a multi-user scenario, each user has access to the CNN to estimate its own channel. The performance of the proposed DL approach is evaluated and compared with state-of-the-art DL-based techniques and its superior performance is demonstrated. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/LWC.2020.2993699 | IEEE Wireless Communications Letters |
Keywords | DocType | Volume |
Deep learning,channel estimation,large intelligent surfaces,massive MIMO | Journal | 9 |
Issue | ISSN | Citations |
9 | 2162-2337 | 18 |
PageRank | References | Authors |
0.61 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ahmet M. Elbir | 1 | 91 | 11.29 |
| Papazafeiropoulos A | 2 | 18 | 0.61 |
| Kourtessis P. | 3 | 18 | 0.61 |
| Symeon Chatzinotas | 4 | 1849 | 192.76 |