MATLAB Code for LOS/NLOS Identification

The LOS/NLOS identification MATLAB code provides a computational framework for determining the existence of direct line-of-sight propagation between communication nodes. This implementation typically utilizes signal characteristics such as received signal strength (RSS), channel impulse response, or time-of-arrival measurements to classify propagation conditions. The core algorithm may employ machine learning classifiers (like SVM or decision trees) or statistical threshold-based methods to distinguish between LOS and NLOS scenarios. Key functions include signal preprocessing to extract relevant features, feature normalization to ensure consistent analysis, and classification modules that output binary results (LOS=1, NLOS=0). The code structure often incorporates channel modeling components to simulate various environmental conditions, allowing users to validate the identification accuracy under different obstacle configurations. This tool is particularly valuable for wireless communication system design, enabling engineers to optimize antenna placement, assess signal blockage scenarios, and develop appropriate mitigation strategies. The implementation supports performance analysis through metrics like classification accuracy, precision-recall curves, and confusion matrix visualization. By integrating this code into communication system simulations, developers can enhance link reliability assessment and improve overall network planning efficiency for applications ranging from 5G networks to indoor positioning systems.