Impact of Channel Imperfection on the Performance of RIS-Assisted Energy-Efficient Hybrid Precoding

Reconfigurable intelligent surface (RIS)-assisted multiuser millimeter-wave (mmWave) wireless systems have emerged as cost-effective and energy-efficient wireless solutions for the future 6G communications. In this paper, RIS-assisted energy-efficient hybrid precoding schemes are introduced as a promising technology that can achieve a transmission in an intelligent way. In addition, an RIS-assisted machine learning (ML) inspired energy-efficient hybrid precoding scheme that requires low-cost and energy-efficient switches and inverters is considered, where the adaptive cross entropy (ACE) optimization algorithm is employed to find the corresponding optimal precoding weights. Besides, imperfect channel state information (CSI) in both the source-to-RIS channel and RIS-to-destination channel links is assumed to be known. Extensive simulations have been conducted to evaluate the effect of imperfect CSI on the achievable sum-rate and the energy efficiency of the proposed RIS-assisted hybrid precoding schemes. Additionally, the performance of the proposed hybrid precoders is compared to that of the conventional hybrid precoding schemes. The results reveal that the proposed hybrid precoding architectures outperform the conventional schemes with large number of reflecting elements, and the performance is significantly degraded in the presence of imperfect CSI.