  Symbol Level Precoding (SLP) has attracted significant research interest due
to its ability to exploit interference for energy-efficient transmission. This
paper proposes an unsupervised deep-neural network (DNN) based SLP framework.
Instead of naively training a DNN architecture for SLP without considering the
specifics of the optimization objective of the SLP domain, our proposal unfolds
a power minimization SLP formulation based on the interior point method (IPM)
proximal `log' barrier function. Furthermore, we extend our proposal to a
robust precoding design under channel state information (CSI) uncertainty. The
results show that our proposed learning framework provides near-optimal
performance while reducing the computational cost from O(n7.5) to O(n3) for the
symmetrical system case where n = number of transmit antennas = number of
users. This significant complexity reduction is also reflected in a
proportional decrease in the proposed approach's execution time compared to the
SLP optimization-based solution.
