  End-to-end autoencoder (AE) learning has the potential of exceeding the
performance of human-engineered transceivers and encoding schemes, without a
priori knowledge of communication-theoretic principles. In this work, we aim to
understand to what extent and for which scenarios this claim holds true when
comparing with fair benchmarks. Our particular focus is on memoryless
multiple-input multiple-output (MIMO) and multi-user (MU) systems. Four case
studies are considered: two point-to-point (closed-loop and open-loop MIMO) and
two MU scenarios (MIMO broadcast and interference channels). For the
point-to-point scenarios, we explain some of the performance gains observed in
prior work through the selection of improved baseline schemes that include
geometric shaping as well as bit and power allocation. For the MIMO broadcast
channel, we demonstrate the feasibility of a novel AE method with centralized
learning and decentralized execution. Interestingly, the learned scheme
performs close to nonlinear vector-perturbation precoding and significantly
outperforms conventional zero-forcing. Lastly, we highlight potential pitfalls
when interpreting learned communication schemes. In particular, we show that
the AE for the considered interference channel learns to avoid interference,
albeit in a rotated reference frame. After de-rotating the learned signal
constellation of each user, the resulting scheme corresponds to conventional
time sharing with geometric shaping.
