Performance analysis of IRS-assist dual-hop wireless communication system

Abstract

The IRS-assist or smart radio environment technique is a widely developing technology that network providers can use to establish sustained connectivity between end-user terminals and central data units for the next-generation wireless standards. This article introduced a simple and more accurate link-switching technique for dual-hop communication: a single link-switching threshold (SLST) algorithm to provide an uninterrupted linkage between the transceiver terminals. Depending on the severity of the communicating channel under a dual-hop system, links can do auto switches between themselves and furnish continuous connectivity between end-user terminals. Due to a discrete number of phase shifts of the IRS elements, phase and quantization errors are induced in the channel; the proposed system can also optimize the phase and quantization errors. Besides, this work investigates improving the physical layer performance of the dual-hop wireless communication system under the combined effect of phase shift and quantization error with the introduction of the SLST method. For this particular, three performance metrics have been encountered: the outage probability (OP), average bit error rate (ABER), and average capacity (bits/s/Hz). A new, more accurate mathematical framework using the Meijer’G function has been constructed to evaluate worthwhile analytical derivation. Under analytical calculation, we have assumed the primary link experienced with common Rayleigh fading and the IRS-assist link (IAL) experienced with Nakagami-m distribution due to a large number of reflecting elements in the system. The proposed dual-hop system furnishes noteworthy benefits for each performance metric rather than individual links. Moreover, the suitable selection of quantization level and a number of reflecting elements confirm the exhibition of satisfactory outcomes and minimize the channel hardness of the system. Additionally, numerical simulation results from MATLAB, using Monte Carlo simulation, have been added to validate the analytical outcomes for every performance measure.