# Learning-Based Symbol Level Precoding: A Memory-Efficient Unsupervised Learning Approach

arXiv: https://arxiv.org/abs/2111.08110 · HTML: https://mlsanity.com/paper/2111.08110/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 26 references that are in the map.
- First posted on arXiv 15 Nov 2021.

## Builds on (most cited first, 7 of 7)

- [XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks](https://mlsanity.com/paper/1603.05279/) (2016)
- [Channel Pruning for Accelerating Very Deep Neural Networks](https://mlsanity.com/paper/1707.06168/) (2017)
- [Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations](https://mlsanity.com/paper/1609.07061/) (2016)
- [Ternary Neural Networks for Resource-Efficient AI Applications](https://mlsanity.com/paper/1609.00222/) (2016)
- [Deep Unfolding of a Proximal Interior Point Method for Image Restoration](https://mlsanity.com/paper/1812.04276/) (2018)
- [Complexity-Scalable Neural Network Based MIMO Detection With Learnable Weight Scaling](https://mlsanity.com/paper/1909.06943/) (2019)
- [A Memory-Efficient Learning Framework for SymbolLevel Precoding with Quantized NN Weights](https://mlsanity.com/paper/2110.06542/) (2021)

## Abstract (arXiv)

Symbol level precoding (SLP) has been proven to be an effective means of managing the interference in a multiuser downlink transmission and also enhancing the received signal power. This paper proposes an unsupervised learning based SLP that applies to quantized deep neural networks (DNNs). Rather than simply training a DNN in a supervised mode, our proposal unfolds a power minimization SLP formulation in an imperfect channel scenario using the interior point method (IPM) proximal `log' barrier function. We use binary and ternary quantizations to compress the DNN's weight values. The results show significant memory savings for our proposals compared to the existing full-precision SLP-DNet with significant model compression of ~21x and ~13x for both binary DNN-based SLP (RSLP-BDNet) and ternary DNN-based SLP (RSLP-TDNets), respectively.