Name
Title | ||
|---|---|---|
Multiple Residual Dense Networks for Reconfigurable Intelligent Surfaces Cascaded Channel Estimation |
Abstract | ||
|---|---|---|
Reconfigurable intelligent surface (RIS) constitutes an essential and promising paradigm that relies programmable wireless environment and provides capability for space-intensive communications, due to the use of low-cost massive reflecting elements over the entire surfaces of man-made structures. However, accurate channel estimation is a fundamental technical prerequisite to achieve the huge performance gains from RIS. By leveraging the low rank structure of RIS channels, three practical residual neural networks, named convolutional blind denoising network, convolutional denoising generative adversarial networks and multiple residual dense network, are proposed to obtain accurate channel state information, which can reflect the impact of different methods on the estimation performance. Simulation results reveal the evolution direction of these three methods and reveal their superior performance compared with existing benchmark schemes. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TVT.2021.3132305 | IEEE Transactions on Vehicular Technology |
Keywords | DocType | Volume |
Channel estimation,deep learning,multiple residual dense network,reconfigurable intelligent surface | Journal | 71 |
Issue | ISSN | Citations |
2 | 0018-9545 | 1 |
PageRank | References | Authors |
0.35 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yu Jin | 1 | 29 | 2.46 |
| Jiayi Zhang | 2 | 911 | 74.44 |
| Chongwen Huang | 3 | 751 | 39.38 |
| Liang Yang | 4 | 73 | 4.03 |
| Huahua Xiao | 5 | 1 | 0.69 |
| Bo Ai | 6 | 1581 | 185.94 |
| Zhiqin Wang | 7 | 1 | 0.35 |