# Deep-Learning-based Millimeter-Wave Massive MIMO for Hybrid Precoding

arXiv: https://arxiv.org/abs/1901.06537 · HTML: https://mlsanity.com/paper/1901.06537/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jan 2019.

## Built on by (most cited first, 13 of 13)

- [Model-Driven Deep Learning Based Channel Estimation and Feedback for Millimeter-Wave Massive Hybrid MIMO Systems](https://mlsanity.com/paper/2104.11052/) (2021)
- [Pervasive Machine Learning for Smart Radio Environments Enabled by Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/2205.03793/) (2022)
- [Multi-hop RIS-Empowered Terahertz Communications: A DRL-based Hybrid Beamforming Design](https://mlsanity.com/paper/2101.09137/) (2021)
- [Two-Timescale End-to-End Learning for Channel Acquisition and Hybrid Precoding](https://mlsanity.com/paper/2110.12059/) (2021)
- [Federated Dropout Learning for Hybrid Beamforming With Spatial Path Index Modulation In Multi-User mmWave-MIMO Systems](https://mlsanity.com/paper/2102.07450/) (2021)
- [An Encoder-Decoder Network for Beamforming over Sparse Large-Scale MIMO Channels](https://mlsanity.com/paper/2510.02355/) (2025)
- [Reinforcement Learning based Per-antenna Discrete Power Control for Massive MIMO Systems](https://mlsanity.com/paper/2101.12154/) (2021)
- [Decentralized Beamforming for Cell-Free Massive MIMO with Unsupervised Learning](https://mlsanity.com/paper/2106.16194/) (2021)
- [Federated Channel Learning for Intelligent Reflecting Surfaces With Fewer Pilot Signals](https://mlsanity.com/paper/2205.03196/) (2022)
- [Deep Learning for THz Channel Estimation and Beamforming Prediction via Sub-6GHz Channel](https://mlsanity.com/paper/2411.15589/) (2024)
- [DRL-based Dolph-Tschebyscheff Beamforming in Downlink Transmission for Mobile Users](https://mlsanity.com/paper/2502.01278/) (2025)
- [GNN-Enabled Robust Hybrid Beamforming with Score-Based CSI Generation and Denoising](https://mlsanity.com/paper/2511.06663/) (2025)
- [Efficient Graph Neural Networks for Multicarrier Wideband Hybrid Beamforming Optimization](https://mlsanity.com/paper/2609.09708/) (2026)

## Abstract (arXiv)

Millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) has been regarded to be an emerging solution for the next generation of communications, in which hybrid analog and digital precoding is an important method for reducing the hardware complexity and energy consumption associated with mixed signal components. However, the fundamental limitations of the existing hybrid precoding schemes is that they have high computational complexity and fail to fully exploit the spatial information. To overcome these limitations, this paper proposes, a deep-learning-enabled mmWave massive MIMO framework for effective hybrid precoding, in which each selection of the precoders for obtaining the optimized decoder is regarded as a mapping relation in the deep neural network (DNN). Specifically, the hybrid precoder is selected through training based on the DNN for optimizing precoding process of the mmWave massive MIMO. Additionally, we present extensive simulation results to validate the excellent performance of the proposed scheme. The results exhibit that the DNN-based approach is capable ofminimizing the bit error ratio (BER) and enhancing spectrum efficiency of the mmWave massive MIMO, which achieves better performance in hybrid precoding compared with conventional schemes while substantially reducing the required computational complexity.