# Investigations on Millimeter-Wave Indoor Channel Simulations for 5G Networks
## 1 Introduction  
Today, more than 54% of people live in cities, indicating a significant global development in urbanization. Thanks to the widely used 5G cellular networks' ultra-reliable low latency, gigantic transfer speed, and ultra-high connection density, our lives are set to change. 5G usage covers three main services: enhanced mobile broadband, mission-critical communications, and massive Internet of Things (IoT) [1]. In addition, for improved smartphones, 5G mobile technology has the potential to bring forth new immersive experiences like virtual reality (VR) and augmented reality (AR) with faster, more consistent data rates, lower latency, and lower cost [2]. In 5G systems, multiple input and multiple output (MIMO) technology is utilized along with beamforming techniques to reduce interference and increase reliability [3].  
The demand for high-definition videos and low-latency online gaming is increasing noticeably. Also, mobile data traffic has increased dramatically recently due to the sharp increment in smartphones, tablets, and devices that can be connected/controlled remotely. The emergence of Wi-Fi technology and its associated applications created congestion in the sub-6 GHz band. Therefore, researchers aimed to utilize a different window in the spectrum that can meet the new demands.  
Propagation at mmWave frequencies is quite different from microwave frequencies [4], as the higher frequency increases reflectivity from walls and objects as the wave dimensions become even smaller compared to the object's dimensions; however, diffuse reflection becomes more dominant, while less power can penetrate through objects. Diffraction contributes less to signal strength; therefore, objects create sharp shadows. This is due to the effect of material electrical properties, which change with frequency.  
Signal propagation within indoor environments has received attention in the research community, with models being developed to describe the propagation behavior within these environments [5]. Some models are empirical, where the best fit for measurements is used; numerous empirical models are proposed in [6; 7]; however, these models should be used in similar environments; others are stochastic, where the channel is random at any time, yet stochastic models describe an indoor channel using its statistical characteristics [8; 9; 10; 11; 12]. Furthermore, other models are deterministic, which can estimate channel parameters by solving Maxwell's equations. Generally, the solvers are either the ray-tracing method [13] or finite difference time domain FDTD-based methods [14]; there are also semi-deterministic models, which are hybrids of deterministic and empirical or stochastic models. These models improve the deterministic model's performance [15].  
This paper presents a study on 5G signal propagation behavior in indoor environments at mmWave frequencies; the study considers the use of directional and omnidirectional antennae in LOS and NLOS scenarios. The investigated parameters include path loss exponent (PLE), Root-mean Square (RMS) delay spread (DS), and some temporal channel parameters. This study offers a comparative analysis to enhance our understanding of indoor channel behavior at various operating frequencies within the same environment. The simulation is important as it provides insights into omnidirectional channel behavior at different frequencies, essential for indoor channel planning. This research is beneficial for network engineering designers to account for the impact of walls on indoor network coverage.  
The paper's layout is as follows: Section 1 contains the introduction; Section 2 discusses the methodology and simulation setup; results and discussions are presented in Section 3; and, finally, conclusions are drawn in Section 4.