Name
Title | ||
|---|---|---|
CNN-Based Joint SNR and Doppler Shift Classification Using Spectrogram Images for Adaptive Modulation and Coding |
Abstract | ||
|---|---|---|
This paper proposes a novel convolutional neural network (CNN) based joint classification method to characterize the signal-to-noise power ratio (SNR) and Doppler shift using spectrogram images, in order to enable efficient adaptive modulation and coding (AMC) designs. It is necessary to maintain high communication performances even in stringent environments where transceivers move at high speed d... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TCOMM.2021.3077565 | IEEE Transactions on Communications |
Keywords | DocType | Volume |
Signal to noise ratio,Estimation,Doppler shift,OFDM,Spectrogram,Channel estimation,Receivers | Journal | 69 |
Issue | ISSN | Citations |
8 | 0090-6778 | 3 |
PageRank | References | Authors |
0.39 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shun Kojima | 1 | 3 | 1.06 |
| Kazuki Maruta | 2 | 21 | 23.36 |
| Yi Feng | 3 | 3 | 1.74 |
| Chang-Jun Ahn | 4 | 105 | 32.23 |
| Vahid Tarokh | 5 | 10373 | 1461.51 |