f blockage phenomenon. Furthermore, the penetration loss is highly dependent on the material and tends to increase with frequency. The shadow fading and angular spread parameters are larger and the boundary between LoS and NLoS depends not only on antenna heights but also on the local environment. The small- scale characteristics of the channel such as delay-spread and angular-spread and the multipath richness is somewhat similar over the, which was a good reason for extending the existing 3GPP models to the wider frequency range [24,25]. The goal of channel modeling is to provide accurate mathematical representations of radio propagation to be used in link-level and system-level simulations corresponding to a specific deployment scenario. Since the radio channel can be assumed as linear, it can be described by its impulse response. Once the impulse response is known, one can determine the response of the radio channel to any input signal. The impulse response is usually represented as a power density function of excess delay, measured relative to the first detectable signal. This function is often referred to as a power delay profile. The channel impulse response varies with the position of the receiver and may also vary with time. Therefore, it is usually measured and reported as an average of profiles measured over one wavelength to reduce noise effects, or over several wavelengths to determine a spatial average. It is important to clarify which aver- age is meant, and how the averaging was performed. The propagation effects of a wireless channel can be modeled with a large-scale propagation model combined with a small-scale fading model, where the former models long-term slow- fading characteristics of the wireless channel, such as path loss and shadowing, while the small-scale fading model provides rapid fluctuation behavior of the wireless channel due to multipath and Doppler spread. For a wireless channel with multiple antennas, static beam- forming gain such as the sectorization beam pattern also contribu