# A Family of Deep Learning Architectures for Channel Estimation and Hybrid Beamforming in Multi-Carrier mm-Wave Massive MIMO

arXiv: https://arxiv.org/abs/1912.10036 · HTML: https://mlsanity.com/paper/1912.10036/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Dec 2019.

## Built on by (most cited first, 6 of 6)

- [Terahertz-Band Joint Ultra-Massive MIMO Radar-Communications: Model-Based and Model-Free Hybrid Beamforming](https://mlsanity.com/paper/2103.00328/) (2021)
- [Federated Channel Learning for Intelligent Reflecting Surfaces With Fewer Pilot Signals](https://mlsanity.com/paper/2205.03196/) (2022)
- [Implicit Channel Learning for Machine Learning Applications in 6G Wireless Networks](https://mlsanity.com/paper/2206.12127/) (2022)
- [Revolutionizing Wireless Networks with Federated Learning: A Comprehensive Review](https://mlsanity.com/paper/2308.04404/) (2023)
- [Enhancing User Throughput in Multi-panel mmWave Radio Access Networks for Beam-based MU-MIMO Using a DRL Method](https://mlsanity.com/paper/2603.02745/) (2026)
- [Self-Evolving In-Context Learning for Direct Pilot-to-Beamformer Design in MU-MISO Systems](https://mlsanity.com/paper/2607.11970/) (2026)

## Abstract (arXiv)

Hybrid analog and digital beamforming transceivers are instrumental in addressing the challenge of expensive hardware and high training overheads in the next generation millimeter-wave (mm-Wave) massive MIMO (multiple-input multiple-output) systems. However, lack of fully digital beamforming in hybrid architectures and short coherence times at mm-Wave impose additional constraints on the channel estimation. Prior works on addressing these challenges have focused largely on narrowband channels wherein optimization-based or greedy algorithms were employed to derive hybrid beamformers. In this paper, we introduce a deep learning (DL) approach for channel estimation and hybrid beamforming for frequency-selective, wideband mm-Wave systems. In particular, we consider a massive MIMO Orthogonal Frequency Division Multiplexing (MIMO-OFDM) system and propose three different DL frameworks comprising convolutional neural networks (CNNs), which accept the raw data of received signal as input and yield channel estimates and the hybrid beamformers at the output. We also introduce both offline and online prediction schemes. Numerical experiments demonstrate that, compared to the current state-of-the-art optimization and DL methods, our approach provides higher spectral efficiency, lesser computational cost and fewer number of pilot signals, and higher tolerance against the deviations in the received pilot data, corrupted channel matrix, and propagation environment.