On the Performance of OIRS-Aided VLC Systems with Random UE Orientation for Indoor Consumer IoT Applications: Analysis and Optimization

This paper investigates an optical intelligent reflecting surface (OIRS) assisted indoor Visible Light Communication (VLC) system, considering the combined effects of random user equipment (UE) orientation and random line-of-sight (LoS) blockage. The system is specifically designed for consumer Internet of Things (IoT) applications, focusing on statistical modeling of channel gain of OIRS-aided indoor VLC systems and performance enhancement. We propose a probability density function (PDF) for the equivalent channel gain developed using a convolution-based approach that integrates both LoS and non-line-of-sight (NLoS) signal paths. We model the random LoS blockage due to human presence using the Bernoulli distribution. We derive series-based expressions for key performance metrics, including average bit error rate (ABER), outage probability (OP), and throughput. To enhance the system efficiency, we optimize the achievable data rate (ADR) using the ant colony optimization (ACO) and the sine-cosine algorithm variant (SCA-V). Monte Carlo simulations validate the analytical results, demonstrating performance improvements and providing insights for enhancing the reliability of IoT-enabled systems in consumer environments. We further validate robustness through large-scale tests, with standard deviation and confidence interval analyses confirming the framework’s consistent and reliable performance under varying VLC conditions.