A Combined Stochastic and Physical Framework for Modeling Indoor 5G Millimeter Wave Propagation
This addresses indoor coverage problems for 5G mmWave networks, but it is incremental as it builds on existing stochastic and physical modeling approaches.
The paper tackles indoor 5G millimeter wave coverage challenges by developing a combined stochastic and physical framework called iGeoStat, which simulates propagation efficiently and shows that diffusion critically impacts indoor mmWave propagation with validated results for two major applications.
Indoor coverage is a major challenge for 5G millimeter waves (mmWaves). In this paper, we address this problem through a novel theoretical framework that combines stochastic indoor environment modeling with advanced physical propagation simulation. This approach is particularly adapted to investigate indoor-to-indoor 5G mmWave propagation. Its system implementation, so-called iGeoStat, generates parameterized typical environments that account for the indoor spatial variations, then simulates radio propagation based on the physical interaction between electromagnetic waves and material properties. This framework is not dedicated to a particular environment, material, frequency or use case and aims to statistically understand the influence of indoor environment parameters on mmWave propagation properties, especially coverage and path loss. Its implementation raises numerous computational challenges that we solve by formulating an adapted link budget and designing new memory optimization algorithms. The first simulation results for two major 5G applications are validated with measurement data and show the efficiency of iGeoStat to simulate multiple diffusion in realistic environments, within a reasonable amount of time and memory resources. Generated output maps confirm that diffusion has a critical impact on indoor mmWave propagation and that proper physical modeling is of the utmost importance to generate relevant propagation models.