Indoor and Outdoor Physical Channel Modeling and Efficient Positioning for Reconfigurable Intelligent Surfaces in mmWave Bands

Channel Modeling in RIS-Empowered Wireless Communications

“…Therefore, modeling a physical open-source, and widely applicable mmWave channel for the RIS-assisted systems in indoor and outdoor environments is of great importance to shed light on realistic use-cases of RISs in future wireless networks. From this point of view, a unified narrowband channel model for RIS-assisted systems both in indoor and outdoor environments is introduced in [3], [26], [27] by including the 5G mmWave channel model with a random number of clusters/scatterers and the characteristics of the RIS. Moreover, a physical channel model for RIS-assisted systems is proposed in [28] by considering the currently used technical specifications on sub-6 GHz bands.…”

“…An overview of the aforementioned channel modeling studies and brief explanations of their main contributions and characteristics can be seen in Table I [3], [10]- [27].…”

“…Because of their unique functionality, channel modeling methodology for RISs is different from other counterpart technologies, such as relaying. Although the major steps for generation RIS-assisted channel modeling are included in this work, interested readers are referred to [26], [27] for the comprehensive view of technical details and statistical characteristics of the channel parameters.…”

“…The proposed model considers various indoor and outdoor wireless propagation environments in terms of physical aspects of mmWave frequencies while numerous practical 5G channel model issues are adopted to our channel model [29]. For a considered operating frequency and environment, the number of clusters (C), number of sub-rays per cluster (S c ), and the positions of the clusters can be determined by following the detailed steps and procedures in [27]. More specifically, according to the 5G channel model, the number of clusters and scatterers are determined using the Poisson and uniform distributions with certain parameters, respectively.…”

Phase Configuration Learning in Wireless Networks with Multiple Reconfigurable Intelligent Surfaces

“…We also use the zero-mean complex Gaussian random variable h 0 having variance L d h to model the Rayleigh faded TX-RX channel gain, where L d h is the pathloss attenuation depending on these nodes' distance. Similar to [26], we assume that all M RISs are located close to RX, and consequently, each g m is modeled as a pure Line-Of-Sight (LOS) channel. On the other hand, each h m channel is modeled as Ricean faded due to the presence of S scattering objects among TX and RISs, and as such, it is composed of a LOS channel and multiple non-LOS components.…”