EE Seminar: IRS for multi-user communication: investigating metrics and phase errors
הרישום לסמינר יבוצע באמצעות סריקת הברקוד למודל (יש להיכנס לפני כן למודל, לא באמצעות האפליקציה)- הרישום מסתיים ב- 15:10
Registration to the seminar will be done by scanning the barcode for the Moodle (Please enter ahead to the Moodle, NOT by application)- Registration ends at 15:10
Electrical Engineering Systems Seminar
Speaker: Eitan Ovrutski
M.Sc. student under the supervision of Prof. Ofer Amrani
Monday, 9th June 2025, at 15:00
Room 011, Kitot Building, Faculty of Engineering
IRS for multi-user communication: investigating metrics and phase errors
Abstract
This thesis explores the use of a discretely phased Intelligent Reflecting Surface (IRS) in a multi-user SISO system under a deterministic channel model. We develop a linear-time algorithm for optimizing discrete phase configurations, which consistently outperforms quantized Adam Gradient Descent across various metrics, including network capacity.
We further examine how IRS can regulate Signal-to-Noise Ratio (SNR) distribution in space to simplify MAC layer design and reduce the need for multiple Modulation and Coding Schemes (MCS). Conventional capacity-maximizing strategies are shown to be suboptimal for this purpose. Alternative metrics are proposed and evaluated in both LOS and NLOS scenarios. Monte Carlo simulations show up to 8.5 dB and 15 dB reduction in SNR variability in LOS and NLOS environments, respectively, with minimal loss in mean SNR.
For the mean SNR metric, we present a novel globally optimal solution using recursive equations in a continuous-phase setting.
Lastly, we investigate the effects of phase quantization and Channel State Information (CSI) estimation errors. We show how, under parametric estimation, the IRS gain becomes non-quadratic beyond a certain error threshold and derive performance bounds. Simulations reveal that in realistic settings with estimation errors, adding IRS elements may degrade performance—contrary to ideal assumptions.
