Fault-Resilient RIS Systems: Toward SINR- and Sum-Rate Maximization of IoT Networks Using ML Frameworks

Pixel failures on Reconfigurable Intelligent Surfaces (RISs) cause major challenges in wireless communication systems. These failures are especially problematic for Internet of Things (IoT) networks, where reliability and Quality-of-Service (QoS) are critical parameters. Failed RIS pixels can significantly impact system efficiency. These failures disrupt signal quality, reduce Signal-to-Interference-Plus-Noise-Ratio (SINR), lower the sum rate, and affect energy efficiency. As a result, maintaining stable communication links becomes challenging. The primary function of RIS is to improve the connectivity and signal strength between the IoT devices and the Base Station (BS). However, when a large number of pixels fail, this function is compromised. Motivated by the aforementioned challenges, this paper focuses on severe RIS pixel failures. The method uses a Deep Neural Network (DNN) to detect failed RIS pixels and a mathematical model to optimize the configuration of the affected regions, including the failed pixels and their neighboring healthy elements. To achieve this, a dynamically adapting mask is employed, ensuring that the total failed pixels within the mask always remain at 25 % or less. The remaining functional elements retain their standard configuration, allowing for minimal and targeted adjustments. This selective reconfiguration focuses on improving the SINR and sum rate at the BS, while minimizing adjustments to ensure optimal performance. The effectiveness of the proposed method is validated through comprehensive experimental evaluations in various scenarios and time instances. The results consistently demonstrate that the proposed Pixel and Configuration (P&C) RIS method improves the SINR and sum-rate performance at the BS, outperforming conventional RIS systems. This adaptive approach offers a resilient and scalable solution to address the challenges posed by faulty RIS elements, ensuring high-quality communication in dynamic and real-world IoT applications.