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(n<sup>7.5</sup>) to O(n<sup>3</sup>
) 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.
An Unsupervised Deep Unfolding Framework for Robust Symbol Level Precoding
