The growing requests for extremely fast indoor wireless connectivity have introduced significant challenges in designing next‐generation communication systems to build Internet of things (IoT)–enabled fully connected smart environments, particularly at higher frequency bands such as millimeter wave (mmWave/MMW). Accurate channel modeling is critical for optimizing these systems, especially in indoor environments where reflections, diffractions, and penetration losses considerably impact signal propagation. This study presents a detailed channel modeling approach using ray tracing techniques to characterize mmWave signal behavior in complex indoor scenarios. To accurately capture essential parameters including path loss, delay spread, and angular spread, the approach simulates signal interactions with environmental elements (e.g., walls, floors, and furniture) by leveraging three‐dimensional (3D) building models. The study provides a deeper understanding of line‐of‐sight (LOS) and non‐line‐of‐sight (NLOS) propagation. Furthermore, it comprehensively compares the propagation characteristics of various frequency bands, ranging from sub‐6 GHz (e.g., 2.4 and 6 GHz) to mmWave (e.g., 28, 60, and 100 GHz), thereby highlighting their distinct behaviors under identical indoor conditions and user trajectories. Using ray tracing, channel impulse responses and path loss metrics are extracted, and coverage map of received power is proposed for each position. Results demonstrate that mmWave bands experience higher path losses than sub‐6 GHz frequencies and are significantly affected by shadowing and blockage. This study not only validates the accuracy of the ray tracing model against empirical data but also demonstrates its utility in designing robust mmWave communication systems, optimizing network deployments, and enhancing beamforming strategies for future 5G and 6G networks.
Yao et al. (Thu,) studied this question.