# Compression of Site-Specific Deep Neural Networks for Massive MIMO Precoding

arXiv: https://arxiv.org/abs/2502.08758 · HTML: https://mlsanity.com/paper/2502.08758/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 15 references that are in the map.
- First posted on arXiv 12 Feb 2025.

## Builds on (most cited first, 8 of 8)

- [Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding](https://mlsanity.com/paper/1510.00149/) (2015)
- [Learned Step Size Quantization](https://mlsanity.com/paper/1902.08153/) (2019)
- [PACT: Parameterized Clipping Activation for Quantized Neural Networks](https://mlsanity.com/paper/1805.06085/) (2018)
- [Minimum Energy Quantized Neural Networks](https://mlsanity.com/paper/1711.00215/) (2017)
- [Efficient Bitwidth Search for Practical Mixed Precision Neural Network](https://mlsanity.com/paper/2003.07577/) (2020)
- [Energy Efficiency Maximization Precoding for Quantized Massive MIMO Systems](https://mlsanity.com/paper/2108.03048/) (2021)
- [SAGE-HB: Swift Adaptation and Generalization in Massive MIMO Hybrid Beamforming](https://mlsanity.com/paper/2401.10513/) (2024)
- [Learning Energy-Efficient Hardware Configurations for Massive MIMO Beamforming](https://mlsanity.com/paper/2308.06376/) (2023)

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

- [A Low-Complexity Plug-and-Play Deep Learning Model for Generalizable Massive MIMO Precoding](https://mlsanity.com/paper/2601.21897/) (2026)

## Abstract (arXiv)

The deployment of deep learning (DL) models for precoding in massive multiple-input multiple-output (mMIMO) systems is often constrained by high computational demands and energy consumption. In this paper, we investigate the compute energy efficiency of mMIMO precoders using DL-based approaches, comparing them to conventional methods such as zero forcing and weighted minimum mean square error (WMMSE). Our energy consumption model accounts for both memory access and calculation energy within DL accelerators. We propose a framework that incorporates mixed-precision quantization-aware training and neural architecture search to reduce energy usage without compromising accuracy. Using a ray-tracing dataset covering various base station sites, we analyze how site-specific conditions affect the energy efficiency of compressed models. Our results show that deep neural network compression generates precoders with up to 35 times higher energy efficiency than WMMSE at equal performance, depending on the scenario and the desired rate. These results establish a foundation and a benchmark for the development of energy-efficient DL-based mMIMO precoders.