Dynamic Metasurface Grouping for IRS Optimization in Massive MIMO Communications

Abstract

Intelligent Reflecting Surfaces (IRSs) grant the ability to control what was once consideredthe uncontrollable part of wireless communications, the channel. These smart signal mirrors show promise to significantly improve the effective signal-to-noise-ratio (SNR) of cell-users when the line-of-sight (LOS) channel between the base station (BS) and user is blocked. IRSs use implementable optimized phase shifts that beamform a reflected signal around channel blockages, and because they are passive devices, they have the benefit of having low cost and low power consumption. Previous works have concluded that IRSs need several hundred elements to outperform relays. Unfortunately, overhead and complexity costs related to optimizing these devices limit their scope to single-input single-output (SISO) systems. With multiple-input multiple-output (MIMO) and Massive MIMO becoming crucial components to modern 5G and beyond networks, a way to mitigate these overhead costs and integrate IRS technology with the promising MIMO techniques is paramount for these devices to have a place within modern cell technologies. This thesis proposes an IRS element grouping scheme that greatly reduces the number of unique IRS phases that need to be calculated and sent to the IRS controller via the limited rate feedback channel and allows for the ideal number of groups to be obtained at the BS before data transmission. Three methods are proposed to design the phase shifts and element partitioning within our scheme to improve effective SNR in an IRS-aided system. In our simulations, it is shown that our best performing method is one that dynamically partitions the IRS elements into non-uniform groups based on information gathered from the reflected channel and then optimizes its phase shifts. This method successfully handles the overhead trade-off problem, and shows significant achievable rate improvement from previous works.