Learning a Physical Layer Scheme for the MIMO Interference Channel

This paper presents a novel physical layer scheme for multiple-input multiple-output (MIMO) communication systems based on unsupervised deep learning (DL) using an autoencoder in an interference channel (IC) environment. Moreover, it extends the single-input single-output (SISO) channel autoencoder to consider fading channel conditions. In both schemes, two physical layer communication system encoders and decoders are jointly optimized in the presence of interference to minimize their symbol error rate (SER). We analyze resulting SER performance for varying signal-to-interference-plus-noise-ratio (SINR) levels. Realistic channel effects; i.e. Rayleigh fading, are used while training the autoencoder system. For SISO systems, the autoencoder system in IC demonstrates significant performance improvement compared to the conventional single-user systems by eliminating interference when there is channel state information (CSI) at the transmitter. The MIMO autoencoder system also shows significant performance improvements compared to the conventional single-user MIMO systems at SINR levels higher than 16dB. MIMO systems with different number of antennas are simulated to analyze the change in the system complexity and scalability. The information required at the transmitter; i.e. CSI from both the intended and interference links, and the autoencoder training time increases with increasing number of antennas for the autoencoder-based MIMO systems.