# Machine Learning-Based Channel Prediction for RIS-assisted MIMO Systems With Channel Aging

arXiv: https://arxiv.org/abs/2406.07387 · HTML: https://mlsanity.com/paper/2406.07387/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 16 references that are in the map.
- First posted on arXiv 9 May 2024.

## Builds on (most cited first, 8 of 8)

- [Reconfigurable Intelligent Surfaces: Principles and Opportunities](https://mlsanity.com/paper/2007.03435/) (2020)
- [Intelligent Reflecting Surface Aided Wireless Communications: A Tutorial](https://mlsanity.com/paper/2007.02759/) (2020)
- [An Optimal Channel Estimation Scheme for Intelligent Reflecting Surfaces based on a Minimum Variance Unbiased Estimator](https://mlsanity.com/paper/1909.09440/) (2019)
- [Two-Timescale Channel Estimation for Reconfigurable Intelligent Surface Aided Wireless Communications](https://mlsanity.com/paper/1912.07990/) (2019)
- [Intelligent Reflecting Surface Enhanced Wireless Network: Joint Active and Passive Beamforming Design](https://mlsanity.com/paper/1809.01423/) (2018)
- [A Hardware Architecture for Reconfigurable Intelligent Surfaces with Minimal Active Elements for Explicit Channel Estimation](https://mlsanity.com/paper/2002.10371/) (2020)
- [Time-Varying Channel Prediction for RIS-Assisted MU-MISO Networks via Deep Learning](https://mlsanity.com/paper/2111.04971/) (2021)
- [Performance Impact of Channel Aging and Phase Noise on Intelligent Reflecting Surface](https://mlsanity.com/paper/2209.10360/) (2022)

## Abstract (arXiv)

Reconfigurable intelligent surfaces (RISs) have emerged as a promising technology to enhance the performance of sixth-generation (6G) and beyond communication systems. The passive nature of RISs and their large number of reflecting elements pose challenges to the channel estimation process. The associated complexity further escalates when the channel coefficients are fast-varying as in scenarios with user mobility. In this paper, we propose an extended channel estimation framework for RIS-assisted multiple-input multiple-output (MIMO) systems based on a convolutional neural network (CNN) integrated with an autoregressive (AR) predictor. The implemented framework is designed for identifying the aging pattern and predicting enhanced estimates of the wireless channels in correlated fast-fading environments. Insightful simulation results demonstrate that our proposed CNN-AR approach is robust to channel aging, exhibiting a high-precision estimation accuracy. The results also show that our approach can achieve high spectral efficiency and low pilot overhead compared to traditional methods.