Robust Transmission Design for IRS-Aided MISO Network with Reflection Coefficient Mismatch

Intelligent reflection surface (IRS) has been recognized as a revolutionary technology to achieve spectrum and energy efficient wireless communications due to its capability to reconfigure the propagation channels. However, due to the limited cost and space of each reflection element, it is difficult to accurately adjust the reflection coefficients of the passive elements. In this paper, we propose a worst-case robust reflection coefficient design for an IRS-aided single-user multiple-input single-output (SU-MISO) system where one IRS is deployed to enhance the received signal quality. Based on the fact of imperfect adjustment of reflection coefficients, our goal is to minimize the transmission power subject to the signal-noise ratio (SNR) constraint on the receiver end and the unit-modulus constraints on the reflection coefficients. The resulting optimization problem is non-convex and in general hard to solve. To tackle this problem, we adopt the linear approximation and alternating optimization (AO) methods to convert the original optimization problem into a sequence of convex subproblems that could be efficiently solved. We then extend our work to a practical situation where only limited phase shifts at each element are available. Numerical results demonstrate the robustness of the transmission scheme and show that high resolution for phase shifts is not an essential condition to approach the ideal performance.

Automated summary

This paper proposes a robust reflection coefficient design for an IRS-aided system to enhance signal quality, addressing the imperfect adjustment of reflection coefficients. By linear approximation and alternating optimization methods, the non-convex optimization problem is converted into a sequence of convex subproblems for efficient solutions. Numerical results demonstrate that high resolution for phase shifts is not necessary for approaching ideal performance.