# Unsupervised Deep Learning for Massive MIMO Hybrid Beamforming

arXiv: https://arxiv.org/abs/2007.00038 · HTML: https://mlsanity.com/paper/2007.00038/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jun 2020.

## Built on by (most cited first, 10 of 10)

- [Data-Driven Deep Learning Based Hybrid Beamforming for Aerial Massive MIMO-OFDM Systems with Implicit CSI](https://mlsanity.com/paper/2201.06778/) (2022)
- [An Encoder-Decoder Network for Beamforming over Sparse Large-Scale MIMO Channels](https://mlsanity.com/paper/2510.02355/) (2025)
- [Meta-Learning Based Optimization for Large Scale Wireless Systems](https://mlsanity.com/paper/2407.01823/) (2024)
- [A Low-Complexity Plug-and-Play Deep Learning Model for Massive MIMO Precoding Across Sites](https://mlsanity.com/paper/2502.08757/) (2025)
- [A Foundation Model for Massive MIMO Precoding with an Adaptive per-User Rate-Power Tradeoff](https://mlsanity.com/paper/2507.18587/) (2025)
- [Decentralized Beamforming for Cell-Free Massive MIMO with Unsupervised Learning](https://mlsanity.com/paper/2106.16194/) (2021)
- [Flexible Unsupervised Learning for Massive MIMO Subarray Hybrid Beamforming](https://mlsanity.com/paper/2208.05443/) (2022)
- [Evolution of MAC Protocols in the Machine Learning Decade: A Comprehensive Survey](https://mlsanity.com/paper/2302.13876/) (2023)
- [A Low-Complexity Plug-and-Play Deep Learning Model for Generalizable Massive MIMO Precoding](https://mlsanity.com/paper/2601.21897/) (2026)
- [AgenticPrecoding: LLM-Empowered Multi-Agent System for Precoding Optimization](https://mlsanity.com/paper/2605.06443/) (2026)

## Abstract (arXiv)

Hybrid beamforming is a promising technique to reduce the complexity and cost of massive multiple-input multiple-output (MIMO) systems while providing high data rate. However, the hybrid precoder design is a challenging task requiring channel state information (CSI) feedback and solving a complex optimization problem. This paper proposes a novel RSSI-based unsupervised deep learning method to design the hybrid beamforming in massive MIMO systems. Furthermore, we propose i) a method to design the synchronization signal (SS) in initial access (IA); and ii) a method to design the codebook for the analog precoder. We also evaluate the system performance through a realistic channel model in various scenarios. We show that the proposed method not only greatly increases the spectral efficiency especially in frequency-division duplex (FDD) communication by using partial CSI feedback, but also has near-optimal sum-rate and outperforms other state-of-the-art full-CSI solutions.