  The recently emerged symbol-level precoding (SLP) technique has been regarded
as a promising solution in multi-user wireless communication systems, since it
can convert harmful multi-user interference (MUI) into beneficial signals for
enhancing system performance. However, the tremendous computational complexity
of conventional symbol-level precoding designs severely hinders the practical
implementations. In order to tackle this difficulty, we propose a novel deep
learning (DL) based approach to efficiently design the symbol-level precoders.
Particularly, in this correspondence, we consider a multi-user multi-input
single-output (MU-MISO) downlink system. An efficient precoding neural network
(EPNN) is introduced to optimize the symbol-level precoders for maximizing the
minimum quality-of-service (QoS) of all users under the power constraint.
Simulation results demonstrate that the proposed EPNN based SLP design can
dramatically reduce the computing time at the price of slight performance loss
compared with the conventional convex optimization based SLP design.
