Enhanced Iterative Hard Thresholding-Based Cascaded Channel Estimation in IRS-Enabled mmWave MIMO Systems

International Journal of Electronics and Communication Engineering

© 2026 by SSRG - IJECE Journal

Volume 13 Issue 2

Year of Publication : 2026

Authors : Poornima Sriramula, L. Nirmala Devi, A. Nageswar Rao

10.14445/23488549/IJECE-V13I2P112

How to Cite?

Poornima Sriramula, L. Nirmala Devi, A. Nageswar Rao, "Enhanced Iterative Hard Thresholding-Based Cascaded Channel Estimation in IRS-Enabled mmWave MIMO Systems," SSRG International Journal of Electronics and Communication Engineering, vol. 13,  no. 2, pp. 158-165, 2026. Crossref, https://doi.org/10.14445/23488549/IJECE-V13I2P112

Abstract:

The integration of Intelligent Reflecting Surfaces (IRS) with millimeter-Wave (mmWave) Multiple-Input Multiple-Output (MIMO) architectures presents a compelling strategy for improving coverage and boosting spectral efficiency in next-generation wireless systems. A central difficulty in such networks lies in estimating the cascaded channel that incorporates the base station (BS)–IRS and IRS–User Equipment (UE) links. This task is particularly challenging because IRS elements operate passively and lack inherent baseband processing capability. In this work, we introduce an Enhanced Iterative Hard Thresholding (EIHT)–based framework for cascaded channel estimation that is computationally efficient and well-suited for large antenna arrays. By representing the IRS-assisted propagation path in the angular domain, the estimation problem is formulated as a sparse recovery task. The proposed algorithm refines the solution through residual-guided updates using pseudo-inverse projections, eliminating the need for explicit support detection or prior channel statistics. Numerical evaluations show that across a range of SNR levels, the proposed method achieves lower Normalized Mean Square Error (NMSE) and faster convergence than benchmark approaches such as Orthogonal Matching Pursuit (OMP) and classical Iterative Hard Thresholding (IHT). These results position the method as a practical and scalable option for real-time deployment in IRS-enabled mmWave MIMO networks.

Keywords:

Millimeter Wave, Multiple input multiple output, Intelligent Reflecting Surface, Channel estimation, Iterative Hard Thresholding, Sparse signal recovery.

References:

[1] Theodore S. Rappaport et al., “Wireless Communications and Applications above 100 GHz: Opportunities and Challenges for 6G and Beyond,” IEEE Access, vol. 7, pp. 78729-78757, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Harsh Tataria et al., “6G Wireless Systems: Vision, Requirements, Challenges, Insights, and Opportunities,” Proceedings of the IEEE, vol. 109, no. 7, pp. 1166-1199, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Thorsten Wild, Volker Braun, and Harish Viswanathan, “Joint Design of Communication and Sensing for Beyond 5G and 6G Systems,” IEEE Access, vol. 9, pp. 30845-30857, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Wei Hong et al., “The Role of Millimeter-Wave Technologies in 5G/6G Wireless Communications,” IEEE Journal of Microwaves, vol. 1, no. 1, pp. 101-122, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Wei Wang, and Wei Zhang, “Joint Beam Training and Positioning for Intelligent Reflecting Surfaces Assisted Millimeter Wave Communications,” IEEE Transactions on Wireless Communications, vol. 20, no. 10, pp. 6282-6297, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Tian Lin et al., “Channel Estimation for IRS-Assisted Millimeter-Wave MIMO Systems: Sparsity-Inspired Approaches,” IEEE Transactions on Communications, vol. 70, no. 6, pp. 4078-4092, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Xiaoling Hu, Rui Zhang, and Caijun Zhong, “Semi-Passive Elements Assisted Channel Estimation for Intelligent Reflecting Surface-Aided Communications,” IEEE Transactions on Wireless Communications, vol. 21, no. 2, pp. 1132-1142, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Wuqiong Zhao et al., “OMPL-SBL Algorithm for Intelligent Reflecting Surface-Aided mmWave Channel Estimation,” IEEE Transactions on Vehicular Technology, vol. 72, no. 11, pp. 15121-15126, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Vidya Bhasker Shukla, Vimal Bhatia, and Kwonhue Choi, “Cascaded Channel Estimator for IRS-Aided mmWave Hybrid MIMO System,” IEEE Wireless Communications Letters, vol. 13, no. 3, pp. 622-626, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Jinming Wen et al., “A Pseudo-Inverse-Based Hard Thresholding Algorithm for Sparse Signal Recovery,” IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 7, pp. 7621-7630, 2023.
[CrossRef] [Google Scholar] [Publisher Link]