FixOM-SAM: Blockage Analysis in Indoor Li-Fi Networks

Blockages in indoor Li-Fi networks can significantly impact the quality of service (QoS) by disrupting the line-of-sight (LOS) communication channels. This paper proposes two novel approaches for modeling blockage effects in Li-Fi networks: Fixed Obstacle Modeling (FixOM) and Shadow-Aware Blockage Modeling (SAM). FixOM focuses on stationary obstacles, and evaluates obstruction probabilities using geometric analysis of LOS paths based on blockage height, radius, and position. SAM extends this analysis by incorporating partial and complete shadow effects, providing a more realistic representation of network performance degradation. Both approaches are applied to a simulated indoor environment with predefined Li-Fi access point configurations, accounting for practical blockage scenarios. Simulation results quantify the impact of blockages on transmission success probability, highlighting the critical role of static and shadow-based obstructions in network planning. This work advances the understanding of blockage effects in Li-Fi communications, offering robust methodologies for performance modeling and aiding in the design of resilient, high-quality indoor communication networks.