Joint Channel Estimation for RIS-Assisted Wireless Communication System

The reconfigurable intelligent surface (RIS) is regarded as a promising technology for the future networks, since it has the ability to manipulate the randomly behaving wireless communication environment. However, due to the large number of the passive RIS elements, the pilot overhead of channel estimation in the RIS-assisted communication system is huge. To address this issue, we propose a three-step orthogonal matching pursuit (OMP) based algorithm which can further improve the channel estimation accuracy compared to the existing pilot-saving schemes. The proposed algorithm can overcome the effect of noise by expanding the number of training samples for the estimation of the same channel parameters. Specifically, the joint sparsity structure of the cascaded channels associated with different users is fully revealed and exploited in this paper. Based on this sparsity structure, the classical OMP algorithm is modified to jointly estimate channel parameters. Simulation result shows that, to achieve the same channel estimate accuracy, the pilot overhead of channel estimation of the proposed algorithm is lower than the existing schemes.

Automated summary

The reconfigurable intelligent surface (RIS) has the ability to manipulate wireless communication environment, but suffers from high pilot overhead in channel estimation due to the large number of passive elements. To address this, we propose an improved algorithm that enhances channel estimation accuracy and reduces pilot overhead.