Abstract
In this letter, we propose ConvAE, a new channel autoencoder structure. ConvAE uses residual blocks with convolutional layers. This configuration increases performance while decreasing computational complexity at run-Time compared with conventional channel autoencoders. The simulations using both conventional and proposed autoencoders for a 2-by-2 multiple-input multiple-output (MIMO) system under Rayleigh and Nakagami-m fading show that the ConvAE is able to attain a lower bit error rate and higher achievable rate relative to the conventional channel autoencoder schemes.
| Original language | English |
|---|---|
| Article number | 8768327 |
| Pages (from-to) | 1769-1772 |
| Number of pages | 4 |
| Journal | IEEE Communications Letters |
| Volume | 23 |
| Issue number | 10 |
| DOIs | |
| State | Published - Oct 2019 |
Keywords
- Autoencoder
- convolutional neural network
- deep learning
- multiple input multiple output