Name
Abstract | ||
|---|---|---|
Channel state information (CSI) feedback is critical for frequency division duplex (FDD) massive multi-input multi-output (MIMO) systems. Most conventional algorithms are based on compressive sensing (CS) and are highly dependent on the level of channel sparsity. To address the issue, a recent approach adopts deep learning (DL) to compress CSI into a codeword with low dimensionality, which has shown much better performance than the CS algorithms when feedback link is perfect. In practical scenario, however, there exists various interference and non-linear effect. In this article, we design a DL-based denoise network, called DNNet, to improve the performance of channel feedback. Numerical results show that the DL-based feedback algorithm with the proposed DNNet has superior performance over the existing algorithms, especially at low signal-to-noise ratio (SNR). |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/LCOMM.2020.2989499 | IEEE Communications Letters |
Keywords | DocType | Volume |
Deep learning,CSI feedback,denoise,massive MIMO | Journal | 24 |
Issue | ISSN | Citations |
8 | 1089-7798 | 8 |
PageRank | References | Authors |
0.45 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hongyuan Ye | 1 | 8 | 0.45 |
| Feifei Gao | 2 | 3093 | 212.03 |
| Jing Qian | 3 | 8 | 0.79 |
| Hao Wang | 4 | 8 | 0.79 |
| Geoffrey Ye Li | 5 | 9071 | 660.27 |