Conformal Intelligent Reflecting Surfaces-Assisted Networks With Reflection Constraints

The integration of intelligent reflecting surfaces (IRS) onto structures takes advantage of its electrically thin and flexible properties, effectively transforming original obstructions into potential serving nodes. However, existing works model IRS as a planar array, which is not well coupled with the infrastructural shape, posing challenges to future large-scale deployment. Therefore, this paper introduces conformal IRS and proposes a reflection constraint model owing to the curvature imparted by the conformal deployment of IRS, based on which the conformal IRS-assisted network is modeled and system-level performance analysis is derived via stochastic geometry. Specifically, considering infrastructure facades’ non-planar feature, to balance the accuracy and analytical tractability, randomly located infrastructures are modeled as cylinders based on the Boolean scheme, and conformal IRSs are deployed around a fraction $\rho $ of them. In addition, the surface curvature of conformal IRS modulates the distribution of Line-of-Sight paths within the system and its effective illumination. We obtain the successfully reflecting probability and the effective reflecting elements respectively under two different states of IRS perfect reflecting and random scattering, and further derive the coverage probability. Numerical results show that conformal IRS significantly improves network connectivity, though it slightly reduces coverage compared to 2D planar IRS.