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

Zhen Chen, Jie Tang, Xiu Yin Zhang, Daniel Ka Chun So, Shi Jin, Kai-Kit Wong

Research output: Contribution to journalArticlepeer-review

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 multioutput (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.
Original languageEnglish
JournalIEEE Transactions on Wireless Communications
Publication statusAccepted/In press - 10 Aug 2021

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