# Deep-Learning Channel Estimation for IRS-Assisted Integrated Sensing and Communication System

arXiv: https://arxiv.org/abs/2402.09441 · HTML: https://mlsanity.com/paper/2402.09441/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 45 references that are in the map.
- First posted on arXiv 29 Jan 2024.

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

- [A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects](https://mlsanity.com/paper/2004.02806/) (2020)
- [Integrated Sensing and Communications: Towards Dual-functional Wireless Networks for 6G and Beyond](https://mlsanity.com/paper/2108.07165/) (2021)
- [Intelligent Reflecting Surface Enhanced Wireless Network via Joint Active and Passive Beamforming](https://mlsanity.com/paper/1810.03961/) (2018)
- [Intelligent Reflecting Surface Aided Wireless Communications: A Tutorial](https://mlsanity.com/paper/2007.02759/) (2020)
- [Learning to Reflect and to Beamform for Intelligent Reflecting Surface with Implicit Channel Estimation](https://mlsanity.com/paper/2009.14404/) (2020)
- [Joint Radar and Communication Design: Applications, State-of-the-art, and the Road Ahead](https://mlsanity.com/paper/1906.00789/) (2019)
- [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)
- [Cram'er-Rao Bound Optimization for Joint Radar-Communication Design](https://mlsanity.com/paper/2101.12530/) (2021)
- [Radar-assisted Predictive Beamforming for Vehicular Links: Communication Served by Sensing](https://mlsanity.com/paper/2001.09306/) (2020)
- [Cascaded Channel Estimation for Large Intelligent Metasurface Assisted Massive MIMO](https://mlsanity.com/paper/1905.07948/) (2019)
- [Bayesian Predictive Beamforming for Vehicular Networks: A Low-overhead Joint Radar-Communication Approach](https://mlsanity.com/paper/2005.07698/) (2020)
- [Intelligent Reflecting Surface-Enhanced OFDM: Channel Estimation and Reflection Optimization](https://mlsanity.com/paper/1909.03272/) (2019)
- [Intelligent Reflecting Surface-Assisted Millimeter Wave Communications: Joint Active and Passive Precoding Design](https://mlsanity.com/paper/1908.10734/) (2019)
- [Capacity Characterization for Intelligent Reflecting Surface Aided MIMO Communication](https://mlsanity.com/paper/1910.01573/) (2019)
- [Deep Reinforcement Learning for Optimizing RIS-Assisted HD-FD Wireless Systems](https://mlsanity.com/paper/2110.04859/) (2021)
- [Leveraging Sensing at the Infrastructure for mmWave Communication](https://mlsanity.com/paper/1911.09796/) (2019)
- [Low Complexity Neural Network Structures for Self-Interference Cancellation in Full-Duplex Radio](https://mlsanity.com/paper/2009.11361/) (2020)
- [Deep Learning-based Time-varying Channel Estimation for RIS Assisted Communication](https://mlsanity.com/paper/2108.05661/) (2021)
- [Hybrid-Layers Neural Network Architectures for Modeling the Self-Interference in Full-Duplex Systems](https://mlsanity.com/paper/2110.09997/) (2021)
- [Reconfigurable Intelligent Surface Optimization for Uplink Sparse Code Multiple Access](https://mlsanity.com/paper/2111.15110/) (2021)
- [Deep Learning Based RIS Channel Extrapolation with Element-grouping](https://mlsanity.com/paper/2105.06850/) (2021)
- [Joint Active and Passive Beamforming for IRS-Assisted Radar](https://mlsanity.com/paper/2108.04095/) (2021)

## Built on by (most cited first, 2 of 2)

- [Deep Learning-based Techniques for Integrated Sensing and Communication Systems: State-of-the-Art, Challenges, and Opportunities](https://mlsanity.com/paper/2509.06968/) (2025)
- [Security-aware Semantic-driven ISAC via Paired Adversarial Residual Networks](https://mlsanity.com/paper/2509.20835/) (2025)

## Abstract (arXiv)

Integrated sensing and communication (ISAC), and intelligent reflecting surface (IRS) are envisioned as revolutionary technologies to enhance spectral and energy efficiencies for next wireless system generations. For the first time, this paper focuses on the channel estimation problem in an IRS-assisted ISAC system. This problem is challenging due to the lack of signal processing capacity in passive IRS, as well as the presence of mutual interference between sensing and communication (SAC) signals in ISAC systems. A three-stage approach is proposed to decouple the estimation problem into sub-ones, including the estimation of the direct SAC channels in the first stage, reflected communication channel in the second stage, and reflected sensing channel in the third stage. The proposed three-stage approach is based on a deep-learning framework, which involves two different convolutional neural network (CNN) architectures to estimate the channels at the full-duplex ISAC base station. Furthermore, two types of input-output pairs to train the CNNs are carefully designed, which affect the estimation performance under various signal-to-noise ratio conditions and system parameters. Simulation results validate the superiority of the proposed estimation approach compared to the least-squares baseline scheme, and its computational complexity is also analyzed.