SVD-based Channel Estimation Performance Analysis of IRS-Assisted MIMO IoT Systems

In this study, we propose an innovative Singular Value Decomposition (SVD)-based channel estimation algorithm for Intelligent Reflecting Surface (IRS)-assisted Internet of Things (IoT) systems. As IoT networks grow increasingly complex, traditional matched-filter (MF) techniques struggle with multipath propagation and interference. To address these challenges, our proposed SVD-based method effectively decomposes the channel matrix into singular values and vectors, enabling more accurate channel estimation. By incorporating IRS into the wireless communication model, our algorithm leverages SVD decomposition to extract critical channel features, mitigating the impact of multipath effects and environmental interference. Furthermore, SVD demonstrates superior noise resistance, significantly outperforming MF-based approaches in high-noise conditions. The ability to reduce noise components through matrix decomposition ensures reliable and stable performance across varying signal-to-noise ratio (SNR) levels. Simulation results validate the effectiveness of our approach, showing significant improvements in estimation accuracy and system performance. The proposed method enhances wireless connectivity and robustness, making it well-suited for next-generation IoT applications. This research provides a promising solution for optimizing IRS-assisted communication systems, paving the way for more efficient and resilient wireless networks in future IoT deployments.