An Unsupervised Deep Unfolding Framework for robust Symbol Level Precoding

TL;DR

This paper introduces an unsupervised deep learning framework for symbol level precoding that optimizes energy efficiency and robustness under channel uncertainty, significantly reducing computational complexity.

Contribution

It develops a novel deep unfolding approach based on the interior point method for SLP, extending it to robust designs under CSI uncertainty, with substantial complexity and time savings.

Findings

Near-optimal performance achieved

Computational complexity reduced from O(n7.5) to O(n3)

Execution time significantly decreased

Abstract

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.