Performance analysis of intelligent reflecting surface-assisted mobile wireless networks subjected to generalized Gaussian noise

Different from the prior works, this paper presents the performance analysis of an intelligent reflecting surface (IRS)-assisted communication link in a static and mobile scenario impaired by Rayleigh fading and additive white generalized Gaussian noise (AWGGN). Precisely, the IRS is configured as an intelligent access point, and the mobile behavior of the nodes is characterized by the random waypoint (RWP) model. To this end, closed-form expressions of average bit error rate (BER), average channel capacity (ACC), and outage probability (OP) in both static and mobile scenarios are obtained. To gain further insight into the system performance at high signal-to-noise ratio (SNR), asymptotic expressions are obtained. Moreover, the effect of the number of reflective elements (N$$ N $$) and the shaping parameter (& lambda;$$ \lambda $$) on the system performance is thoroughly studied. The results indicate that introduction of IRS leads to significant improvement in the overall system performance. The derived results are corroborated with Monte Carlo simulations.