# Accelerating and Compressing Deep Neural Networks for Massive MIMO CSI Feedback

arXiv: https://arxiv.org/abs/2304.01914 · HTML: https://mlsanity.com/paper/2304.01914/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 19 references that are in the map.
- First posted on arXiv 20 Jan 2023.

## 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)
- [Learning both Weights and Connections for Efficient Neural Networks](https://mlsanity.com/paper/1506.02626/) (2015)
- [SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size](https://mlsanity.com/paper/1602.07360/) (2016)
- [Fast Sparse ConvNets](https://mlsanity.com/paper/1911.09723/) (2019)
- [Lightweight Convolutional Neural Networks for CSI Feedback in Massive MIMO](https://mlsanity.com/paper/2005.00438/) (2020)
- [Compression and Acceleration of Neural Networks for Communications](https://mlsanity.com/paper/1907.13269/) (2019)
- [Graph Neural Networks for Channel Decoding](https://mlsanity.com/paper/2207.14742/) (2022)
- [Deep Learning-Based Synchronization for Uplink NB-IoT](https://mlsanity.com/paper/2205.10805/) (2022)

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

- [Contrastive Learning and Adversarial Disentanglement for Privacy-Aware Task-Oriented Semantic Communication](https://mlsanity.com/paper/2410.22784/) (2024)
- [Adaptive Pareto-Optimal Token Merging for Edge Transformer Models in Semantic Communication](https://mlsanity.com/paper/2509.09168/) (2025)

## Abstract (arXiv)

The recent advances in machine learning and deep neural networks have made them attractive candidates for wireless communications functions such as channel estimation, decoding, and downlink channel state information (CSI) compression. However, most of these neural networks are large and inefficient making it a barrier for deployment in practical wireless systems that require low-latency and low memory footprints for individual network functions. To mitigate these limitations, we propose accelerated and compressed efficient neural networks for massive MIMO CSI feedback. Specifically, we have thoroughly investigated the adoption of network pruning, post-training dynamic range quantization, and weight clustering to optimize CSI feedback compression for massive MIMO systems. Furthermore, we have deployed the proposed model compression techniques on commodity hardware and demonstrated that in order to achieve inference gains, specialized libraries that accelerate computations for sparse neural networks are required. Our findings indicate that there is remarkable value in applying these model compression techniques and the proposed joint pruning and quantization approach reduced model size by 86.5% and inference time by 76.2% with minimal impact to model accuracy. These compression methods are crucial to pave the way for practical adoption and deployments of deep learning-based techniques in commercial wireless systems.