# Deep Channel Learning For Large Intelligent Surfaces Aided mm-Wave Massive MIMO Systems

arXiv: https://arxiv.org/abs/2001.11085 · HTML: https://mlsanity.com/paper/2001.11085/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jan 2020.

## Built on by (most cited first, 19 of 19)

- [Generative AI for Physical Layer Communications: A Survey](https://mlsanity.com/paper/2312.05594/) (2023)
- [Pervasive Machine Learning for Smart Radio Environments Enabled by Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/2205.03793/) (2022)
- [Learning to Estimate RIS-Aided mmWave Channels](https://mlsanity.com/paper/2107.12631/) (2021)
- [Time-Varying Channel Prediction for RIS-Assisted MU-MISO Networks via Deep Learning](https://mlsanity.com/paper/2111.04971/) (2021)
- [Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insights](https://mlsanity.com/paper/2312.04688/) (2023)
- [Decision Transformers for RIS-Assisted Systems with Diffusion Model-Based Channel Acquisition](https://mlsanity.com/paper/2501.08007/) (2025)
- [Federated Dropout Learning for Hybrid Beamforming With Spatial Path Index Modulation In Multi-User mmWave-MIMO Systems](https://mlsanity.com/paper/2102.07450/) (2021)
- [Deep-Learning Channel Estimation for IRS-Assisted Integrated Sensing and Communication System](https://mlsanity.com/paper/2402.09441/) (2024)
- [Graph Attention Networks for Channel Estimation in RIS-assisted Satellite IoT Communications](https://mlsanity.com/paper/2104.00735/) (2021)
- [Channel Estimation and Hybrid Architectures for RIS-Assisted Communications](https://mlsanity.com/paper/2104.07115/) (2021)
- [Untrained DNN for Channel Estimation of RIS-Assisted Multi-User OFDM System with Hardware Impairments](https://mlsanity.com/paper/2107.07423/) (2021)
- [Deep-Learning-Based Channel Estimation for IRS-Assisted ISAC System](https://mlsanity.com/paper/2402.09439/) (2024)
- [Extreme Learning Machine-based Channel Estimation in IRS-Assisted Multi-User ISAC System](https://mlsanity.com/paper/2402.09440/) (2024)
- [Unsupervised Learning-Based Joint Resource Allocation and Beamforming Design for RIS-Assisted MISO-OFDMA Systems](https://mlsanity.com/paper/2506.22448/) (2025)
- [Beyond 5G RIS mmWave Systems: Where Communication and Localization Meet](https://mlsanity.com/paper/2109.07729/) (2021)
- [Federated Channel Learning for Intelligent Reflecting Surfaces With Fewer Pilot Signals](https://mlsanity.com/paper/2205.03196/) (2022)
- [Machine Learning for Metasurfaces Design and Their Applications](https://mlsanity.com/paper/2211.01296/) (2022)
- [Lightweight Deep Learning-Based Channel Estimation for RIS-Aided Extremely Large-Scale MIMO Systems on Resource-Limited Edge Devices](https://mlsanity.com/paper/2507.09627/) (2025)
- [A 3GPP-Compliant Benchmark Dataset for RIS-Aided Beyond 5G Networks](https://mlsanity.com/paper/2609.39058/) (2026)

## Abstract (arXiv)

This letter presents the first work introducing a deep learning (DL) framework for channel estimation in large intelligent surface (LIS) assisted massive MIMO (multiple-input multiple-output) systems. A twin convolutional neural network (CNN) architecture is designed and it is fed with the received pilot signals to estimate both direct and cascaded channels. In a multi-user scenario, each user has access to the CNN to estimate its own channel. The performance of the proposed DL approach is evaluated and compared with state-of-the-art DL-based techniques and its superior performance is demonstrated.