Channel Estimation of IRS-Aided Communication Systems with Hybrid Multiobjective Optimization

Abstract

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 in mmWave channels is exploited. By utilizing the properties of Kronecker products, IRS-assisted mmWave channel estimation are converted into a sparse signal recovery problem, which involves two competing cost function terms (measurement error and a 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 algorithm achieves competitive error performance compared to existing channel estimation algorithms.

Reconfigurable-Intelligent-Surface-Assisted B5G/6G Wireless Communications: Challenges, Solution, and Future Opportunities

“…The phase shifts of the RIS is designed to adjust the propagation directions of the incident signals. To fully realize a substantial training overhead reduction, the inherent sparsity of mmWave channel is exploited to formulate the cascade channel construction by exploiting the compressed sensing (CS) [13]. Under the proposed framework, the cascaded channel estimation problem is formulated as a CS-based sparse reconstruction problem, which includes three conflicting objectives: the measurement error, the number of reflecting elements and the sparse constraint.…”

Robust Hybrid Beamforming Design for Multi-RIS Assisted MIMO System With Imperfect CSI

“…Due to the significant potentials posed by the RIS, various RIS-assisted wireless communications were extensively studied, such as passive beamforming designs [6]- [9], RISassisted channel estimation [10] and so on. Particularly, the authors in [11] exploited RIS to control the wireless propagation environment and enhance the coverage and transmission rate.…”