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 |
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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