  Deep learning (DL) based autoencoder has shown great potential to
significantly enhance the physical layer performance. In this paper, we present
a DL based autoencoder for interference channel. Based on a characterization of
a k-user Gaussian interference channel, where the interferences are classified
as different levels from weak to very strong interferences based on a coupling
parameter {\alpha}, a DL neural network (NN) based autoencoder is designed to
train the data set and decode the received signals. The performance such a DL
autoencoder for different interference scenarios are studied, with {\alpha}
known or partially known, where we assume that {\alpha} is predictable but with
a varying up to 10\% at the training stage. The results demonstrate that DL
based approach has a significant capability to mitigate the effect induced by a
poor signal-to-noise ratio (SNR) and a high interference-to-noise ratio (INR).
However, the enhancement depends on the knowledge of {\alpha} as well as the
interference levels. The proposed DL approach performs well with {\alpha} up to
10\% offset for weak interference level. For strong and very strong
interference channel, the offset of {\alpha} needs to be constrained to less
than 5\% and 2\%, respectively, to maintain similar performance as {\alpha} is
known.

Machine Learning;Machine Learning