# Extreme Learning Machine-based Channel Estimation in IRS-Assisted Multi-User ISAC System

arXiv: https://arxiv.org/abs/2402.09440 · HTML: https://mlsanity.com/paper/2402.09440/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 50 references that are in the map.
- First posted on arXiv 29 Jan 2024.

## Builds on (most cited first, 24 of 24)

- [Integrated Sensing and Communications: Towards Dual-functional Wireless Networks for 6G and Beyond](https://mlsanity.com/paper/2108.07165/) (2021)
- [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)
- [Bayesian Predictive Beamforming for Vehicular Networks: A Low-overhead Joint Radar-Communication Approach](https://mlsanity.com/paper/2005.07698/) (2020)
- [Reconfigurable Intelligent Surfaces for Wireless Communications: Principles, Challenges, and Opportunities](https://mlsanity.com/paper/2005.00938/) (2020)
- [Intelligent Reflecting Surface-Assisted Millimeter Wave Communications: Joint Active and Passive Precoding Design](https://mlsanity.com/paper/1908.10734/) (2019)
- [An Optimal Channel Estimation Scheme for Intelligent Reflecting Surfaces based on a Minimum Variance Unbiased Estimator](https://mlsanity.com/paper/1909.09440/) (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)
- [Waveform Design and Performance Analysis for Full-Duplex Integrated Sensing and Communication](https://mlsanity.com/paper/2108.06449/) (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)
- [Intelligent Reflection Enabling Technologies for Integrated and Green Internet-of-Everything Beyond 5G: Communication, Sensing, and Security](https://mlsanity.com/paper/2205.03182/) (2022)
- [Cascaded Channel Estimation for IRS-assisted Mmwave Multi-antenna with Quantized Beamforming](https://mlsanity.com/paper/2010.00865/) (2020)
- [Enhanced ELM Based Channel Estimation for RIS-Assisted OFDM systems with Insufficient CP and Imperfect Hardware](https://mlsanity.com/paper/2110.13433/) (2021)
- [Joint transmit and reflective beamforming for IRS-assisted integrated sensing and communication](https://mlsanity.com/paper/2111.13511/) (2021)

## Built on by (most cited first, 1 of 1)

- [Deep Learning-based Techniques for Integrated Sensing and Communication Systems: State-of-the-Art, Challenges, and Opportunities](https://mlsanity.com/paper/2509.06968/) (2025)

## Abstract (arXiv)

Multi-user integrated sensing and communication (ISAC) assisted by intelligent reflecting surface (IRS) has been recently investigated to provide a high spectral and energy efficiency transmission. This paper proposes a practical channel estimation approach for the first time to an IRS-assisted multiuser ISAC system. The estimation problem in such a system is challenging since the sensing and communication (SAC) signals interfere with each other, and the passive IRS lacks signal processing ability. A two-stage approach is proposed to transfer the overall estimation problem into sub-ones, successively including the direct and reflected channels estimation. Based on this scheme, the ISAC base station (BS) estimates all the SAC channels associated with the target and uplink users, while each downlink user estimates the downlink communication channels individually. Considering a low-cost demand of the ISAC BS and downlink users, the proposed two-stage approach is realized by an efficient neural network (NN) framework that contains two different extreme learning machine (ELM) structures to estimate the above SAC channels. Moreover, two types of input-output pairs to train the ELMs are carefully devised, which impact the estimation accuracy and computational complexity under different system parameters. Simulation results reveal a substantial performance improvement achieved by the proposed ELM-based approach over the least-squares and NN-based benchmarks, with reduced training complexity and faster training speed.