  This paper proposes an unsupervised learning-based precoding framework that
trains deep neural networks (DNNs) with no target labels by unfolding an
interior point method (IPM) proximal `log' barrier function. The proximal `log'
barrier function is derived from the strict power minimization formulation
subject to signal-to-interference-plus-noise ratio (SINR) constraint. The
proposed scheme exploits the known interference via symbol-level precoding
(SLP) to minimize the transmit power and is named strict Symbol-Level-Precoding
deep network (SLP-SDNet). The results show that SLP-SDNet outperforms the
conventional block-level-precoding (Conventional BLP) scheme while achieving
near-optimal performance faster than the SLP optimization-based approach
