1.
Chien, Trinh Van; Viet, Nguyen Hoang; Chatzinotas, Symeon; Hanzo, Lajos
Improved Differential Evolution for Enhancing the Aggregated Channel Estimation of RIS-Aided Cell-Free Massive MIMO Journal Article
In: IEEE Transactions on Vehicular Technology, pp. 1–6, 2025, ISSN: 1939-9359.
Abstract | Links | BibTeX | Tags: Cell-free massive MIMO, Channel estimation, Closed-form solutions, Contamination, Correlation, differential evolution, Massive MIMO, Optimization, Rayleigh channels, reconfigurable intelligent surface, Reconfigurable Intelligent Surfaces, Training, Vectors
@article{chien_improved_2025,
title = {Improved Differential Evolution for Enhancing the Aggregated Channel Estimation of RIS-Aided Cell-Free Massive MIMO},
author = {Trinh Van Chien and Nguyen Hoang Viet and Symeon Chatzinotas and Lajos Hanzo},
url = {https://ieeexplore.ieee.org/document/11080325},
doi = {10.1109/TVT.2025.3589240},
issn = {1939-9359},
year = {2025},
date = {2025-01-01},
urldate = {2025-10-08},
journal = {IEEE Transactions on Vehicular Technology},
pages = {1–6},
abstract = {Cell-Free Massive multiple-input multiple-output (MIMO) systems are investigated with the support of a reconfigurable intelligent surface (RIS). The RIS phase shifts are designed for improved channel estimation in the presence of spatial correlation. Specifically, we formulate the channel estimate and estimation error expressions using linear minimum mean square error (LMMSE) estimation for the aggregated channels. An optimization problem is then formulated to minimize the average normalized mean square error (NMSE) subject to practical phase shift constraints. To circumvent the problem of inherent nonconvexity, we then conceive an enhanced version of the differential evolution algorithm that is capable of avoiding local minima by introducing an augmentation operator applied to some high-performing Diffential Evolution (DE) individuals. Numerical results indicate that our proposed algorithm can significantly improve the channel estimation quality of the state-of-the-art benchmarks.},
keywords = {Cell-free massive MIMO, Channel estimation, Closed-form solutions, Contamination, Correlation, differential evolution, Massive MIMO, Optimization, Rayleigh channels, reconfigurable intelligent surface, Reconfigurable Intelligent Surfaces, Training, Vectors},
pubstate = {published},
tppubtype = {article}
}
Cell-Free Massive multiple-input multiple-output (MIMO) systems are investigated with the support of a reconfigurable intelligent surface (RIS). The RIS phase shifts are designed for improved channel estimation in the presence of spatial correlation. Specifically, we formulate the channel estimate and estimation error expressions using linear minimum mean square error (LMMSE) estimation for the aggregated channels. An optimization problem is then formulated to minimize the average normalized mean square error (NMSE) subject to practical phase shift constraints. To circumvent the problem of inherent nonconvexity, we then conceive an enhanced version of the differential evolution algorithm that is capable of avoiding local minima by introducing an augmentation operator applied to some high-performing Diffential Evolution (DE) individuals. Numerical results indicate that our proposed algorithm can significantly improve the channel estimation quality of the state-of-the-art benchmarks.