# Variational Autoencoders for Precoding Matrices with High Spectral Efficiency

arXiv: https://arxiv.org/abs/2111.15626 · HTML: https://mlsanity.com/paper/2111.15626/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 37 references that are in the map.
- First posted on arXiv 23 Nov 2021.

## Builds on (most cited first, 13 of 13)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Variational Autoencoders for Collaborative Filtering](https://mlsanity.com/paper/1802.05814/) (2018)
- [Neural Variational Inference for Text Processing](https://mlsanity.com/paper/1511.06038/) (2015)
- [What Will 5G Be?](https://mlsanity.com/paper/1405.2957/) (2014)
- [Conditional Variational Autoencoder for Neural Machine Translation](https://mlsanity.com/paper/1812.04405/) (2018)
- [Blind Channel Equalization using Variational Autoencoders](https://mlsanity.com/paper/1803.01526/) (2018)
- [Deep Energy Autoencoder for Noncoherent Multicarrier MU-SIMO Systems](https://mlsanity.com/paper/2002.08710/) (2020)
- [Neural-Network Optimized 1-bit Precoding for Massive MU-MIMO](https://mlsanity.com/paper/1903.03718/) (2019)
- [Massive MIMO Adaptive Modulation and Coding Using Online Deep Learning Algorithm](https://mlsanity.com/paper/2105.12827/) (2021)
- [Framework on Deep Learning Based Joint Hybrid Processing for mmWave Massive MIMO Systems](https://mlsanity.com/paper/2006.03215/) (2020)
- [Adaptive Regularized Zero-Forcing Beamforming in Massive MIMO with Multi-Antenna Users](https://mlsanity.com/paper/2107.00853/) (2021)
- [Study on Precoding Optimization Algorithms in Massive MIMO System with Multi-Antenna Users](https://mlsanity.com/paper/2107.13440/) (2021)

## Abstract (arXiv)

Neural networks are used for channel decoding, channel detection, channel evaluation, and resource management in multi-input and multi-output (MIMO) wireless communication systems. In this paper, we consider the problem of finding precoding matrices with high spectral efficiency (SE) using variational autoencoder (VAE). We propose a computationally efficient algorithm for sampling precoding matrices with minimal loss of quality compared to the optimal precoding. In addition to VAE, we use the conditional variational autoencoder (CVAE) to build a unified generative model. Both of these methods are able to reconstruct the distribution of precoding matrices of high SE by sampling latent variables. This distribution obtained using VAE and CVAE methods is described in the literature for the first time.