Name
Abstract | ||
|---|---|---|
Channel state information (CSI) feedback plays an important part in frequency division duplex (FDD) massive multiple-input multiple-output (MIMO) systems. However, it is still facing many challenges, e.g., excessive feedback overhead, low feedback accuracy and a large number of training parameters. In this letter, to address these practical concerns, we propose a deep learning (DL)-based CSI feedback scheme, named DS-NLCsiNet. By taking advantage of non-local blocks, DS-NLCsiNet can capture long-range dependencies efficiently. In addition, dense connectivity is adopted to strengthen the feature refinement module. Simulation results demonstrate that DS-NLCsiNet achieves higher CSI feedback accuracy and better reconstruction quality for the same compression ratio, when compared to state-of-the-art compression schemes. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/LCOMM.2020.3019653 | IEEE Communications Letters |
Keywords | DocType | Volume |
Massive MIMO,frequency division duplex (FDD),CSI feedback,non-local neural networks,densely connected convolutional networks | Journal | 24 |
Issue | ISSN | Citations |
12 | 1089-7798 | 4 |
PageRank | References | Authors |
0.40 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaotong Yu | 1 | 4 | 0.40 |
| Xiangyi Li | 2 | 4 | 0.40 |
| Huaming Wu | 3 | 81 | 14.49 |
| Yang Bai | 4 | 68 | 24.51 |