# An Unsupervised Learning-Based Approach for Symbol-Level-Precoding

arXiv: https://arxiv.org/abs/2104.09214 · HTML: https://mlsanity.com/paper/2104.09214/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Apr 2021.

## Built on by (most cited first, 2 of 2)

- [A Memory-Efficient Learning Framework for SymbolLevel Precoding with Quantized NN Weights](https://mlsanity.com/paper/2110.06542/) (2021)
- [Unlocking Symbol-Level Precoding Efficiency Through Tensor Equivariant Neural Network](https://mlsanity.com/paper/2510.02108/) (2025)

## Abstract (arXiv)

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.