Performance of IRS-Assisted MIMO THz System Using Compressed Sensing-Based Measurement Matrix

Abstract

Abstract

Terahertz (THz) communications is a new frontier for the sixth-generation wireless systems due to availability of large bandwidth that supports terabits per second data rates. However, THz signals experience significant attenuation over distance, restricting their applicability primarily to indoor environments with limited range. Additionally, THz systems demand high Nyquist sampling rate, which increases computational complexity at the receiver. To address these challenges, intelligent-reflecting surfaces (IRSs)-assisted multiple-input multiple-output (MIMO) is a possible candidate that controls the propagation direction of THz waves. However, excessive dimensions of IRS and MIMO results in an enlarged nearfield according to Rayleigh distance for THz bands. To mitigate the system complexity and reduce sampling to the sub-Nyquist rate, a low-complexity compressed sensing with transmit beamforming based receiver design is proposed for an IRS-aided MIMO THz system. The proposed approach utilizes an IRS signal-matched (IRSSM) measurement matrix to measure the transmitted signal at sub-Nyquist rate by exploiting sparsity of the waveform and the THz channels at the receiver. Furthermore, a closed-form expression of the average symbol error rate (ASER) is derived over generalized Nakagami-m fading for the considered network. Moreover, obtaining an ideal channel state information (CSI) is challenging in practice; hence, an imperfect CSI from the base station (BS) to the user is also considered. Simulation results demonstrate that the proposed IRSSM measurement matrix outperforms the prevailing matrices for the IRS-assisted MIMO THz systems.