[Paper Review] Indoor and Outdoor Physical Channel Modeling and Efficient Positioning for Reconfigurable Intelligent Surfaces in mmWave Bands

Ertuğrul Başar, Ibrahim Yildirim+1 more|arXiv (Cornell University)|May 31, 2020

Advanced Wireless Communication Technologies31 references47 citations

TL;DR

This paper presents a physical, open-source mmWave RIS channel model applicable to indoor and outdoor environments, including LOS/NLOS, shadowing, and RIS element patterns, plus the SimRIS simulator for RIS-based system design.

ABSTRACT

Reconfigurable intelligent surface (RIS)-assisted communication appears as one of the potential enablers for sixth generation (6G) wireless networks by providing a new degree of freedom in the system design to telecom operators. Particularly, RIS-empowered millimeter wave (mmWave) communication systems can be a remedy to provide broadband and ubiquitous connectivity. This paper aims to fill an important gap in the open literature by providing a physical, accurate, open-source, and widely applicable RIS channel model for mmWave frequencies. Our model is not only applicable in various indoor and outdoor environments but also includes the physical characteristics of wireless propagation in the presence of RISs by considering 5G radio channel conditions. Various deployment scenarios are presented for RISs and useful insights are provided for system designers from the perspective of potential RIS use-cases and their efficient positioning. The scenarios in which the use of an RIS makes a big difference or might not have a big impact on the communication system performance, are revealed. The open-source and comprehensive SimRIS Channel Simulator is also introduced in this paper.

Motivation & Objective

• Develop a fundamental, open-source RIS-augmented channel model for mmWave bands that integrates with 5G physical channel models.
• Provide a unified indoor/outdoor narrowband RIS channel model that includes LOS, shadowing, shared clusters, and RIS element patterns.
• Offer practical insights and guidelines for RIS deployment and positioning in various environments.
• Deliver an open-source MATLAB tool (SimRIS) for tunable RIS-based channel modeling across frequencies and layouts.

Proposed method

• Adopt a cascaded RIS channel model under the far-field assumption with power scaling for RIS-assisted links.
• Construct Tx–RIS and RIS–Rx subchannels using a clustered statistical MIMO approach derived from 3GPP/5G models.
• Represent RIS elements with a cos^q radiation pattern and a uniform square array response to compute steering vectors.
• Incorporate realistic path losses via 5G close-in free-space reference distance models with shadow fading.
• Model LOS probabilities and random cluster/sub-ray parameters to generate realistic indoor/outdoor RIS channels.
• Provide an open-source SimRIS Channel Simulator in MATLAB with tunable frequency, locations, and RIS size.

Figure 1: RIS-assisted communication with $M$ IOs between Tx-RIS.

Experimental results

Research questions

• RQ1 How can RIS-assisted mmWave channels be modeled in a physically accurate way for both indoor and outdoor environments?
• RQ2 What is the impact of RIS geometry, element patterns, and environmental clustering on RIS-enhanced link performance?
• RQ3 How should RISs be positioned to maximize gains in different deployment scenarios?
• RQ4 How can shared clusters and LOS conditions between Tx–RIS and RIS–Rx be incorporated into a unified RIS channel model?

Key findings

• A unified, physically-based narrowband RIS channel model is developed for indoor and outdoor mmWave scenarios, including LOS, shadowing, and shared clusters.
• An open-source SimRIS Channel Simulator is introduced for tunable RIS-based channel modeling.
• The model shows potential RIS gains and use-cases, and provides practical guidelines for efficient RIS deployment and positioning.
• The framework can be extended to include realistic RIS architectures, imperfections, and spatial correlation in later work.
• The approach integrates RIS into state-of-the-art 5G channel models, enabling more realistic RIS-assisted system analyses.

Figure 2: 3D array response geometry for a square RIS with $N$ elements.