Intelligent Reflecting Surface Partitioning-Based Channel Modeling and Performance Analysis

Abstract

In the intelligent reflecting surface (IRS) aided near-field systems, the IRS reflection phases, influenced by the coupling between the IRS units, significantly affect the performance. To improve the performance gains of near-field systems, the channel models based on IRS partitioning scheme are proposed for air-to-ground (A2G) millimeter-wave (mmWave) systems. The corresponding IRS reflection phases are designed, consisting of both the IRS unit-dependent reflection phases and the sub-surface-dependent reflection phases. The designed reflection phases not only compensate for phase variations introduced by the mismatches in the azimuth and elevation angles of IRS units but also for the phase variations due to the existence of distance differences from transceiver to the centers of sub-surfaces. Using the proposed partitioning scheme, the influence of the coupling between the IRS units on the reflection phase can be effectively mitigated. To further explore the impact of the IRS partitioning scheme on performance, a power leakage ratio, which is defined as the ratio of the leakage power using the partitioning scheme to the total received signal power, is introduced. Numerical results show that the received signal power of the proposed channel model matches that of the near-field model using the ideal reflection phases very well.