Wireless Communications with Reconfigurable Intelligent Surface: Path Loss Modeling and Experimental Measurement

Abstract: Reconfigurable intelligent surfaces (RISs) comprised of tunable unit cells have recently drawn significant attention due to their superior capability in manipulating electromagnetic waves. In particular, RIS-assisted wireless communications have the great potential to achieve significant performance improvement and coverage enhancement in a cost-effective and energy-efficient manner, by properly programming the reflection coefficients of the unit cells of RISs. In this paper, free-space path loss models for RIS-assisted wireless communications are developed for different scenarios by studying the physics and electromagnetic nature of RISs. The proposed models, which are first validated through extensive simulation results, reveal the relationships between the free-space path loss of RIS-assisted wireless communications and the distances from the transmitter/receiver to the RIS, the size of the RIS, the near-field/far-field effects of the RIS, and the radiation patterns of antennas and unit cells. In addition, three fabricated RISs (metasurfaces) are utilized to further corroborate the theoretical findings through experimental measurements conducted in a microwave anechoic chamber. The measurement results match well with the modeling results, thus validating the proposed free-space path loss models for RIS, which may pave the way for further theoretical studies and practical applications in this field.

• The paper introduces three path loss models (far-field beamforming, near-field beamforming, and near-field broadcasting) for RIS-assisted communications.
• The paper validates theoretical models with extensive experimental measurements in controlled anechoic chamber setups using fabricated RISs.
• The study redefines near-field and far-field boundaries, offering key insights for optimizing RIS placement and enhancing energy efficiency in wireless systems.

Wireless Communications with Reconfigurable Intelligent Surface: Path Loss Modeling and Experimental Measurement

The paper "Wireless Communications with Reconfigurable Intelligent Surface: Path Loss Modeling and Experimental Measurement" presents a detailed exploration of the potential and challenges of integrating Reconfigurable Intelligent Surfaces (RIS) into wireless communication systems. The authors, Wankai Tang et al., provide not only theoretical models but also practical experimental validations to elaborate on their findings.

Overview

Reconfigurable Intelligent Surfaces (RIS) represent a transformative technology within the field of wireless communication, leveraging programmable metasurfaces to control electromagnetic waves in real-time. These surfaces offer the potential to enhance communication performance significantly, using mechanisms that include reflecting and beamforming signals to optimize wireless link quality.

Path Loss Models

The paper focuses on developing free-space path loss models for RIS-assisted wireless communications. The authors derive these models based on the physics and electromagnetic properties intrinsic to RIS technology. Key findings and methodologies are summarized as follows:

1. General Formula for Received Signal Power: The authors derive a foundational formula to calculate received signal power in RIS-assisted systems, factoring in the distances between the RIS and transmitter/receiver, RIS dimensions, and respected radiation patterns.
2. Specific Path Loss Models: The paper proposes three specific path loss models:
   • Far-Field Beamforming: This model applies when both the transmitter and receiver are in the far-field region of the RIS. The authors demonstrate that the received signal power scales inversely with $(d_1 \cdot d_2)^2$, where $d_1$ and $d_2$ are the distances from the transmitter to the RIS and from the RIS to the receiver, respectively.
   • Near-Field Beamforming: This model addresses scenarios where either the transmitter or the receiver or both are in the near-field region of the RIS. The complexity increases here, as the propagation characteristics become more intricate.
   • Near-Field Broadcasting: In this scenario, RISs operate in the near field to broadcast the signal across a specific area, as opposed to a singular focus point. The modeled path loss here is proportional to $(d_1 + d_2)^2$.
3. Redefinition of Near-Field and Far-Field Boundary: The study redefines the boundary between near-field and far-field regions for RIS-assisted systems, based on their empirical and simulation results.

Validation Through Experimental Measurement

To substantiate the theoretical models, the authors conducted extensive experimental measurements using three different fabricated RISs in a controlled microwave anechoic chamber. Their key experimental setups and findings include:

• Validation of Far-Field Models: Experimental results in cases of both specular and intelligent reflections showed a high alignment with the proposed theoretical models.
• Validation of Near-Field Models: Similarly, experiments conducted in the near-field region also demonstrated a strong correlation between the theoretical predictions and practical measurements.

The experiments effectively verified the analytical models and demonstrated the RIS's capability to control and manipulate wireless signals in both near-field and far-field scenarios.

Practical Implications and Future Directions

• Practical Implications: The validated path loss models provide crucial insights for the integration of RIS in practical communication systems. These models can aid in link budget calculations, system design, and improving energy efficiency by optimizing the placement and control of RIS elements.
• Future Developments: Moving forward, further research could explore RIS technology's potential in dynamic and complex propagation environments, including urban and indoor scenarios. The practical challenges of large-scale deployment, such as power consumption and control signal overhead, also need detailed investigation. Additionally, extending the current models to integrate small-scale fading effects will provide a more comprehensive understanding of RIS-assisted communication systems.

Conclusion

The paper by Tang et al. offers a rigorous analysis and validation of path loss models for RIS-assisted wireless communication. By bridging theoretical modeling with practical measurement, the research paves the way for deeper comprehension and future advancements in intelligent surface-based communication technologies. The findings underline the transformative potential of RIS in enhancing wireless communication performance through innovative, energy-efficient approaches.

In sum, this work serves as a crucial building block, offering foundational models that will be instrumental for researchers and engineers in the further development and practical deployment of RIS technology.