Joint Reflection Design and Channel Estimation for Intelligent Reflective Surfaces

In the era of advanced wireless communication systems like 5G and beyond, Intelligent Reflective Surfaces (IRS) offer a promising solution to boost coverage, throughput, and energy efficiency. This paper investigates the crucial task of channel estimation in IRS-assisted communication, which is essential for optimizing signal propagation. We explore two primary methodologies: the ON/OFF method and the DFT-based method, for joint reflection pattern design and channel estimation. The former activates IRS elements sequentially, while the latter leverages Dense Fourier Transform (DFT) matrices for optimal reflection patterns. Through theoretical analysis and simulation results, we demonstrate the superiority of the DFT -based approach in terms of estimation accuracy, particularly for cascaded channel parameters. Additionally, we observe that extending the estimation period enhances the performance of the DFT -based method, offering valuable insights for practical implementation in future wireless communication systems.