Improved differential evolution for enhancing the aggregated channel estimation of RIS-aided cell-free massive MIMO
Improved differential evolution for enhancing the aggregated channel estimation of RIS-aided cell-free massive MIMO
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.
Chien, Trinh Van
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Viet, Nguyen Hoang
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Chatzinotas, Symeon
e349eceb-5716-490e-900b-563e347746f7
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Chien, Trinh Van
2c4ce5cb-0dc3-4b58-88b7-1dcf5a8ed7b2
Viet, Nguyen Hoang
569a560c-c9f0-45e5-9064-451cc9372c25
Chatzinotas, Symeon
e349eceb-5716-490e-900b-563e347746f7
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Chien, Trinh Van, Viet, Nguyen Hoang, Chatzinotas, Symeon and Hanzo, Lajos
(2025)
Improved differential evolution for enhancing the aggregated channel estimation of RIS-aided cell-free massive MIMO.
IEEE Transactions on Vehicular Technology.
(In Press)
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.
Text
Improved Differential Evolution for Enhancing the Aggregated Channel Estimation of RIS-Aided Cell-Free Massive MIMO
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Accepted/In Press date: 28 June 2025
Identifiers
Local EPrints ID: 504374
URI: http://eprints.soton.ac.uk/id/eprint/504374
ISSN: 0018-9545
PURE UUID: 8ab1ce75-c703-4553-a0b7-a2accabe7f9a
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Date deposited: 08 Sep 2025 16:59
Last modified: 09 Sep 2025 01:33
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Contributors
Author:
Trinh Van Chien
Author:
Nguyen Hoang Viet
Author:
Symeon Chatzinotas
Author:
Lajos Hanzo
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