# Lightweight Deep Learning-Based Channel Estimation for RIS-Aided Extremely Large-Scale MIMO Systems on Resource-Limited Edge Devices

arXiv: https://arxiv.org/abs/2507.09627 · HTML: https://mlsanity.com/paper/2507.09627/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 31 references that are in the map.
- First posted on arXiv 13 Jul 2025.

## Builds on (most cited first, 18 of 18)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation](https://mlsanity.com/paper/2004.08790/) (2020)
- [Federated Learning for Channel Estimation in Conventional and RIS-Assisted Massive MIMO](https://mlsanity.com/paper/2008.10846/) (2020)
- [Deep Channel Learning For Large Intelligent Surfaces Aided mm-Wave Massive MIMO Systems](https://mlsanity.com/paper/2001.11085/) (2020)
- [Deep Residual Learning for Channel Estimation in Intelligent Reflecting Surface-Assisted Multi-User Communications](https://mlsanity.com/paper/2009.01423/) (2020)
- [Rayleigh Fading Modeling and Channel Hardening for Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/2009.04723/) (2020)
- [Towards Massive MIMO 2.0: Understanding spatial correlation, interference suppression, and pilot contamination](https://mlsanity.com/paper/1904.03406/) (2019)
- [Reconfigurable Intelligent Surfaces: Three Myths and Two Critical Questions](https://mlsanity.com/paper/2006.03377/) (2020)
- [Channel Estimation for RIS-Aided Multiuser Millimeter-Wave Systems](https://mlsanity.com/paper/2106.14792/) (2021)
- [Channel Estimation for Reconfigurable Intelligent Surface Aided Multi-User mmWave MIMO Systems](https://mlsanity.com/paper/1912.03619/) (2019)
- [Learning to Estimate RIS-Aided mmWave Channels](https://mlsanity.com/paper/2107.12631/) (2021)
- [Extremely Large-Scale MIMO: Fundamentals, Challenges, Solutions, and Future Directions](https://mlsanity.com/paper/2209.12131/) (2022)
- [Cascaded Channel Estimation for Intelligent Reflecting Surface Assisted Multiuser MISO Systems](https://mlsanity.com/paper/2108.09002/) (2021)
- [Enabling 6G Performance in the Upper Mid-Band by Transitioning From Massive to Gigantic MIMO](https://mlsanity.com/paper/2407.05630/) (2024)
- [Near-Field User Localization and Channel Estimation for XL-MIMO Systems: Fundamentals, Recent Advances, and Outlooks](https://mlsanity.com/paper/2407.10147/) (2024)
- [Blockage Prediction for Mobile UE in RIS-assisted Wireless Networks: A Deep Learning Approach](https://mlsanity.com/paper/2209.11088/) (2022)
- [Convolutional Dictionary Learning Based Hybrid-Field Channel Estimation for XL-RIS-Aided Massive MIMO Systems](https://mlsanity.com/paper/2409.16664/) (2024)
- [Near-Field LoS/NLoS Channel Estimation for RIS-Aided MU-MIMO Systems: Piece-Wise Low-Rank Approximation Approach](https://mlsanity.com/paper/2410.13806/) (2024)

## Abstract (arXiv)

Next-generation wireless technologies such as 6G aim to meet demanding requirements such as ultra-high data rates, low latency, and enhanced connectivity. Extremely Large-Scale MIMO (XL-MIMO) and Reconfigurable Intelligent Surface (RIS) are key enablers, with XL-MIMO boosting spectral and energy efficiency through numerous antennas, and RIS offering dynamic control over the wireless environment via passive reflective elements. However, realizing their full potential depends on accurate Channel State Information (CSI). Recent advances in deep learning have facilitated efficient cascaded channel estimation. However, the scalability and practical deployment of existing estimation models in XL-MIMO systems remain limited. The growing number of antennas and RIS elements introduces a significant barrier to real-time and efficient channel estimation, drastically increasing data volume, escalating computational complexity, requiring advanced hardware, and resulting in substantial energy consumption. To address these challenges, we propose a lightweight deep learning framework for efficient cascaded channel estimation in XL-MIMO systems, designed to minimize computational complexity and make it suitable for deployment on resource-constrained edge devices. Using spatial correlations in the channel, we introduce a patch-based training mechanism that reduces the dimensionality of input to patch-level representations while preserving essential information, allowing scalable training for large-scale systems. Simulation results under diverse conditions demonstrate that our framework significantly improves estimation accuracy and reduces computational complexity, regardless of the increasing number of antennas and RIS elements in XL-MIMO systems.