Performance analysis of channel estimation techniques for IRS assisted MIMO

Abstract

Abstract The need for low latency and high data rates is increasing rapidly since the advent of wireless communication. The current fifth-generation (5G) networks are unable to fulfill the requirements of upcoming technologies. So, researchers are commencing their research beyond 5G. Terahertz (THz) frequency is one candidate to satisfy the large bandwidth requirement and intelligent reflecting surface (IRS) is incorporated to mitigate signal blockage which is the main problem for communication at high frequencies. Channel estimation is a process of identifying coefficients of the channel matrix. The compressive sensing technique is of great importance as it decreases the number of pilot symbols required for channel estimation. As mmWave and THz signals are naturally sparse applying a compressive sensing technique is reasonable. Unlike other papers, this paper considers the imperfect IRS elements, which is the real case, by varying the value of $$\beta$$ β (amplitude perturbations). The channel estimation performance of the conventional least squares (LS), orthogonal matching pursuit (OMP) and Oracle is analyzed with respect to signal-to-noise ratio (SNR) and pilot length (T). Normalized mean square error (NMSE) and spectral efficiency (SE) are used as performance metrics and the OMP algorithm is found to perform better than LS even at a fewer number of pilot symbols.