  We propose a novel deep energy autoencoder (EA) for noncoherent multicarrier
multiuser single-input multipleoutput (MU-SIMO) systems under fading channels.
In particular, a single-user noncoherent EA-based (NC-EA) system, based on the
multicarrier SIMO framework, is first proposed, where both the transmitter and
receiver are represented by deep neural networks (DNNs), known as the encoder
and decoder of an EA. Unlike existing systems, the decoder of the NC-EA is fed
only with the energy combined from all receive antennas, while its encoder
outputs a real-valued vector whose elements stand for the subcarrier power
levels. Using the NC-EA, we then develop two novel DNN structures for both
uplink and downlink NC-EA multiple access (NC-EAMA) schemes, based on the
multicarrier MUSIMO framework. Note that NC-EAMA allows multiple users to share
the same sub-carriers, thus enables to achieve higher performance gains than
noncoherent orthogonal counterparts. By properly training, the proposed NC-EA
and NC-EAMA can efficiently recover the transmitted data without any channel
state information estimation. Simulation results clearly show the superiority
of our schemes in terms of reliability, flexibility and complexity over
baseline schemes.
