Spatial-Time MIMO Autoencoder for Physical Layer

Autoencoder-based communication systems show great potential due to the fast development of Deep Learning. In this letter, we propose an end-to-end communication structure for transmitting spatial-time symbol blocks. For simulation, we build up three schemes of multi-input multi-output(MIMO) systems respectively. The results show that the proposed autoencoder structures are able to learn a regular constellation alphabet and attain a lower bit error rate than conventional communication systems in low signal-to-noise(SNR) situation.