  We propose a deep-learning approach for the joint MIMO detection and channel
decoding problem. Conventional MIMO receivers adopt a model-based approach for
MIMO detection and channel decoding in linear or iterative manners. However,
due to the complex MIMO signal model, the optimal solution to the joint MIMO
detection and channel decoding problem (i.e., the maximum likelihood decoding
of the transmitted codewords from the received MIMO signals) is computationally
infeasible. As a practical measure, the current model-based MIMO receivers all
use suboptimal MIMO decoding methods with affordable computational
complexities. This work applies the latest advances in deep learning for the
design of MIMO receivers. In particular, we leverage deep neural networks (DNN)
with supervised training to solve the joint MIMO detection and channel decoding
problem. We show that DNN can be trained to give much better decoding
performance than conventional MIMO receivers do. Our simulations show that a
DNN implementation consisting of seven hidden layers can outperform
conventional model-based linear or iterative receivers. This performance
improvement points to a new direction for future MIMO receiver design.
