# Deep Learning for Massive MIMO Channel State Acquisition and Feedback

arXiv: https://arxiv.org/abs/2002.06945 · HTML: https://mlsanity.com/paper/2002.06945/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Feb 2020.

## Built on by (most cited first, 3 of 3)

- [Overview of Deep Learning-based CSI Feedback in Massive MIMO Systems](https://mlsanity.com/paper/2206.14383/) (2022)
- [Binarized Aggregated Network with Quantization: Flexible Deep Learning Deployment for CSI Feedback in Massive MIMO System](https://mlsanity.com/paper/2105.00354/) (2021)
- [Structure-Informed Estimation for Pilot-Limited MIMO Channels via Tensor Decomposition](https://mlsanity.com/paper/2602.04083/) (2026)

## Abstract (arXiv)

Massive multiple-input multiple-output (MIMO) systems are a main enabler of the excessive throughput requirements in 5G and future generation wireless networks as they can serve many users simultaneously with high spectral and energy efficiency. To achieve this, massive MIMO systems require accurate and timely channel state information (CSI), which is acquired by a training process that involves pilot transmission, CSI estimation and feedback. This training process incurs a training overhead, which scales with the number of antennas, users and subcarriers. Reducing this training overhead in massive MIMO systems has been a major topic of research since the emergence of the concept. Recently, deep learning (DL)-based approaches for massive MIMO training have been proposed and showed significant improvements compared to traditional techniques. This paper provides an overview of how neural networks (NNs) can be used in the training process of massive MIMO systems to improve the performance by reducing the CSI acquisition overhead and to reduce complexity.