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Hybrid Evolutionary-based Sparse Channel Estimation for IRS-assisted mmWave MIMO Systems

Chen, Z; Tang, J; Zhang, XY; So, DKC; Jin, S; Wong, KK; (2021) Hybrid Evolutionary-based Sparse Channel Estimation for IRS-assisted mmWave MIMO Systems. IEEE Transactions on Wireless Communications 10.1109/TWC.2021.3105405. (In press). Green open access

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Abstract

The intelligent reflecting surface (IRS)-assisted millimeter wave (mmWave) communication system has emerged as a promising technology for coverage extension and capacity enhancement. Prior works on IRS have mostly assumed perfect channel state information (CSI), which facilitates in deriving the upper-bound performance but is difficult to realize in practice due to passive elements of IRS without signal processing capabilities. In this paper, we propose a compressive channel estimation techniques for IRS-assisted mmWave multi-input and multi-output (MIMO) system. To reduce the training overhead, the inherent sparsity of mmWave channels is exploited. By utilizing the properties of Kronecker products, IRS-assisted mmWave channel is converted into a sparse signal recovery problem, which involves two competing cost function terms (measurement error and sparsity term). Existing sparse recovery algorithms solve the combined contradictory objectives function using a regularization parameter, which leads to a suboptimal solution. To address this concern, a hybrid multiobjective evolutionary paradigm is developed to solve the sparse recovery problem, which can overcome the difficulty in the choice of regularization parameter value. Simulation results show that under a wide range of simulation settings, the proposed method achieves competitive error performance compared to existing channel estimation methods.

Type: Article
Title: Hybrid Evolutionary-based Sparse Channel Estimation for IRS-assisted mmWave MIMO Systems
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/TWC.2021.3105405
Publisher version: https://doi.org/10.1109/TWC.2021.3105405
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Intelligent reflecting surface (IRS), millimeter wave communications, channel estimation, compressed sensing, hybrid evolutionary algorithm
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/10134576
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