# Time-Varying Channel Prediction for RIS-Assisted MU-MISO Networks via Deep Learning

arXiv: https://arxiv.org/abs/2111.04971 · HTML: https://mlsanity.com/paper/2111.04971/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 52 references that are in the map.
- First posted on arXiv 9 Nov 2021.

## Builds on (most cited first, 23 of 23)

- [Intelligent Reflecting Surface Enhanced Wireless Network via Joint Active and Passive Beamforming](https://mlsanity.com/paper/1810.03961/) (2018)
- [Enabling Large Intelligent Surfaces with Compressive Sensing and Deep Learning](https://mlsanity.com/paper/1904.10136/) (2019)
- [Holographic MIMO Surfaces for 6G Wireless Networks: Opportunities, Challenges, and Trends](https://mlsanity.com/paper/1911.12296/) (2019)
- [Deep Channel Learning For Large Intelligent Surfaces Aided mm-Wave Massive MIMO Systems](https://mlsanity.com/paper/2001.11085/) (2020)
- [Weighted Sum-Rate Maximization for Reconfigurable Intelligent Surface Aided Wireless Networks](https://mlsanity.com/paper/1912.11999/) (2019)
- [Hybrid Beamforming for Reconfigurable Intelligent Surface based Multi-user Communications: Achievable Rates with Limited Discrete Phase Shifts](https://mlsanity.com/paper/1910.14328/) (2019)
- [Indoor Signal Focusing with Deep Learning Designed Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/1905.07726/) (2019)
- [Beamforming Optimization for Wireless Network Aided by Intelligent Reflecting Surface with Discrete Phase Shifts](https://mlsanity.com/paper/1906.03165/) (2019)
- [Cascaded Channel Estimation for Large Intelligent Metasurface Assisted Massive MIMO](https://mlsanity.com/paper/1905.07948/) (2019)
- [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)
- [Terahertz Massive MIMO with Holographic Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/2009.10963/) (2020)
- [Deep Learning Optimized Sparse Antenna Activation for Reconfigurable Intelligent Surface Assisted Communication](https://mlsanity.com/paper/2009.01607/) (2020)
- [A Hardware Architecture for Reconfigurable Intelligent Surfaces with Minimal Active Elements for Explicit Channel Estimation](https://mlsanity.com/paper/2002.10371/) (2020)
- [Channel Estimation for RIS-Empowered Multi-User MISO Wireless Communications](https://mlsanity.com/paper/2008.01459/) (2020)
- [Weighted Sum-Rate Optimization for Intelligent Reflecting Surface Enhanced Wireless Networks](https://mlsanity.com/paper/1905.07920/) (2019)
- [Channel Estimation for Intelligent Reflecting Surface Assisted Multiuser Communications: Framework, Algorithms, and Analysis](https://mlsanity.com/paper/1912.11783/) (2019)
- [Joint Training of the Superimposed Direct and Reflected Links in Reconfigurable Intelligent Surface Assisted Multiuser Communications](https://mlsanity.com/paper/2105.14484/) (2021)
- [Reconfigurable Intelligent Surface Aided NOMA Networks](https://mlsanity.com/paper/1912.10044/) (2019)
- [Reconfigurable Intelligent Surface assisted Two-Way Communications: Performance Analysis and Optimization](https://mlsanity.com/paper/2001.07907/) (2020)
- [Optimized Energy and Information Relaying in Self-Sustainable IRS-Empowered WPCN](https://mlsanity.com/paper/2004.03108/) (2020)
- [TRICE: A Channel Estimation Framework for RIS-Aided Millimeter-Wave MIMO Systems](https://mlsanity.com/paper/2008.09499/) (2020)
- [Intelligent Reflecting Surface Aided Multiple Access Over Fading Channels](https://mlsanity.com/paper/2006.07090/) (2020)

## Built on by (most cited first, 4 of 4)

- [A Comparison of Neural Networks for Wireless Channel Prediction](https://mlsanity.com/paper/2308.14020/) (2023)
- [LinFormer: A Linear-based Lightweight Transformer Architecture For Time-Aware MIMO Channel Prediction](https://mlsanity.com/paper/2410.21351/) (2024)
- [Machine Learning-Based Channel Prediction for RIS-assisted MIMO Systems With Channel Aging](https://mlsanity.com/paper/2406.07387/) (2024)
- [CSI Prediction Using Diffusion Models](https://mlsanity.com/paper/2510.11214/) (2025)

## Abstract (arXiv)

To mitigate the effects of shadow fading and obstacle blocking, reconfigurable intelligent surface (RIS) has become a promising technology to improve the signal transmission quality of wireless communications by controlling the reconfigurable passive elements with less hardware cost and lower power consumption. However, accurate, low-latency and low-pilot-overhead channel state information (CSI) acquisition remains a considerable challenge in RIS-assisted systems due to the large number of RIS passive elements. In this paper, we propose a three-stage joint channel decomposition and prediction framework to require CSI. The proposed framework exploits the two-timescale property that the base station (BS)-RIS channel is quasi-static and the RIS-user equipment (UE) channel is fast time-varying. Specifically, in the first stage, we use the full-duplex technique to estimate the channel between a BS's specific antenna and the RIS, addressing the critical scaling ambiguity problem in the channel decomposition. We then design a novel deep neural network, namely, the sparse-connected long short-term memory (SCLSTM), and propose a SCLSTM-based algorithm in the second and third stages, respectively. The algorithm can simultaneously decompose the BS-RIS channel and RIS-UE channel from the cascaded channel and capture the temporal relationship of the RIS-UE channel for prediction. Simulation results show that our proposed framework has lower pilot overhead than the traditional channel estimation algorithms, and the proposed SCLSTM-based algorithm can also achieve more accurate CSI acquisition robustly and effectively.