# A Memory-Efficient Learning Framework for SymbolLevel Precoding with Quantized NN Weights

arXiv: https://arxiv.org/abs/2110.06542 · HTML: https://mlsanity.com/paper/2110.06542/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 54 references that are in the map.
- First posted on arXiv 13 Oct 2021.

## Builds on (most cited first, 12 of 12)

- [ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices](https://mlsanity.com/paper/1707.01083/) (2017)
- [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)
- [BinaryConnect: Training Deep Neural Networks with binary weights during propagations](https://mlsanity.com/paper/1511.00363/) (2015)
- [Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations](https://mlsanity.com/paper/1609.07061/) (2016)
- [Learning to Detect](https://mlsanity.com/paper/1805.07631/) (2018)
- [Ternary Neural Networks for Resource-Efficient AI Applications](https://mlsanity.com/paper/1609.00222/) (2016)
- [Deep Learning based Wireless Resource Allocation with Application to Vehicular Networks](https://mlsanity.com/paper/1907.03289/) (2019)
- [Massive MIMO 1-Bit DAC Transmission: A Low-Complexity Symbol Scaling Approach](https://mlsanity.com/paper/1709.08278/) (2017)
- [Complexity-Scalable Neural Network Based MIMO Detection With Learnable Weight Scaling](https://mlsanity.com/paper/1909.06943/) (2019)
- [Interference Exploitation Precoding for Multi-Level Modulations: Closed-Form Solutions](https://mlsanity.com/paper/1811.03289/) (2018)
- [An Unsupervised Learning-Based Approach for Symbol-Level-Precoding](https://mlsanity.com/paper/2104.09214/) (2021)

## Built on by (most cited first, 1 of 1)

- [Learning-Based Symbol Level Precoding: A Memory-Efficient Unsupervised Learning Approach](https://mlsanity.com/paper/2111.08110/) (2021)

## Abstract (arXiv)

This paper proposes a memory-efficient deep neural network (DNN) framework-based symbol level precoding (SLP). We focus on a DNN with realistic finite precision weights and adopt an unsupervised deep learning (DL) based SLP model (SLP-DNet). We apply a stochastic quantization (SQ) technique to obtain its corresponding quantized version called SLP-SQDNet. The proposed scheme offers a scalable performance vs memory tradeoff, by quantizing a scale-able percentage of the DNN weights, and we explore binary and ternary quantizations. Our results show that while SLP-DNet provides near-optimal performance, its quantized versions through SQ yield 3.46x and 2.64x model compression for binary-based and ternary-based SLP-SQDNets, respectively. We also find that our proposals offer 20x and 10x computational complexity reductions compared to SLP optimization-based and SLP-DNet, respectively.