Path Loss Modeling and Measurements for Reconfigurable Intelligent Surfaces in the Millimeter-Wave Frequency Band (2101.08607v2)

Abstract: Reconfigurable intelligent surfaces (RISs) provide an interface between the electromagnetic world of wireless propagation environments and the digital world of information science. Simple yet sufficiently accurate path loss models for RISs are an important basis for theoretical analysis and optimization of RIS-assisted wireless communication systems. In this paper, we refine our previously proposed free-space path loss model for RISs to make it simpler, more applicable, and easier to use. The impact of the antenna's directivity of the transmitter, receiver, and the unit cells of the RIS on the path loss is explicitly formulated as an angle-dependent loss factor. The refined model gives more accurate estimates of the path loss of RISs comprised of unit cells with a deep sub-wavelength size. Based on the proposed model, the properties of a single unit cell are evaluated in terms of scattering performance, power consumption, and area, which allows us to unveil fundamental considerations for deploying RISs in high frequency bands. Two fabricated RISs operating in the millimeter-wave (mmWave) band are utilized to carry out a measurement campaign. The measurement results are shown to be in good agreement with the proposed path loss model. In addition, the experimental results suggest an effective form to characterize the power radiation pattern of the unit cell for path loss modeling.

• The paper refines free-space path loss models by incorporating antenna radiation patterns and the sub-wavelength dimensions of RIS unit cells.
• The evaluation shows that energy efficiency of a single RIS unit cell decreases with the square of frequency while power consumption per unit area increases.
• Experimental measurements with two fabricated RISs validate the refined model, demonstrating improved prediction accuracy over traditional models.

Path Loss Modeling and Measurements for Reconfigurable Intelligent Surfaces in the Millimeter-Wave Frequency Band

This paper develops and refines path loss models for Reconfigurable Intelligent Surfaces (RISs) operating in the Millimeter-Wave (mmWave) frequency band, aiming to optimize RIS-assisted wireless communication systems. The significance of this work lies in its attempt to simplify existing models while enhancing their practical applicability and ease of use. The proposed models address key factors such as the directivity of antennas and the sub-wavelength size of RIS unit cells, yielding more accurate predictions of path loss.

Key Findings

1. Model Refinements: The paper introduces refinements to existing free-space path loss models, taking into account the radiation patterns of transmit/receive antennas and RIS unit cells. These refinements become particularly impactful when considering RISs comprised of unit cells with deep sub-wavelength dimensions.
2. Properties of Single Unit Cell: The paper evaluates performance metrics for single RIS unit cells, including scattering efficiency, power consumption, and area considerations. It reveals that energy efficiency is inversely proportional to the square of the operating frequency, while power consumption per unit area is directly proportional to it. This poses fundamental challenges when deploying RISs in high frequency bands.
3. Measurement Campaign: Utilizing two fabricated RISs, the paper validates the path loss model through experimental measurements in the mmWave band. The measurement results corroborate the accuracy of the refined model against traditional path loss models.

Implications and Future Directions

The introduction of refined path loss models has both practical and theoretical implications. Practically, the models can enhance the efficiency and reduce the computational complexity of RIS deployment in mmWave bands. Theoretically, they offer insights into the impact of the electromagnetic properties and geometrical configuration of RISs on wireless channel characterization.

As for future developments, the deployment of RISs in higher frequency bands such as Terahertz (THz) may benefit significantly from the presented refined models. New challenges will arise, such as increased vulnerability to atmospheric absorption and scattering, which will necessitate further enhancements to path loss models. Additionally, integrating machine learning algorithms could potentially optimize RIS configurations dynamically, adapting to changing environmental conditions to maintain optimal signal propagation.