Large-Scale RIS Enabled Air-Ground Channels: Near-Field Modeling and Analysis

TL;DR

This paper introduces a near-field channel model for large-scale RIS-enabled UAV-to-vehicle communication, using sub-array partitioning to balance complexity and accuracy, and analyzes key propagation characteristics.

Contribution

It proposes a novel sub-array partition framework and a beam domain channel model for near-field RIS channels, improving modeling efficiency and accuracy.

Findings

The model accurately captures near-field and far-field channel features.

Simulation results show effective tradeoff between complexity and accuracy.

The framework enhances understanding of RIS channel propagation characteristics.

Abstract

Existing works mainly rely on the far-field planar-wave-based channel model to assess the performance of reconfigurable intelligent surface (RIS)-enabled wireless communication systems. However, when the transmitter and receiver are in near-field ranges, this will result in relatively low computing accuracy. To tackle this challenge, we initially develop an analytical framework for sub-array partitioning. This framework divides the large-scale RIS array into multiple sub-arrays, effectively reducing modeling complexity while maintaining acceptable accuracy. Then, we develop a beam domain channel model based on the proposed sub-array partition framework for large-scale RIS-enabled UAV-to-vehicle communication systems, which can be used to efficiently capture the sparse features in RIS-enabled UAV-to-vehicle channels in both near-field and far-field ranges. Furthermore, some important propagation characteristics of the proposed channel model, including the spatial cross-correlation functions (CCFs), temporal auto-correlation functions (ACFs), frequency correlation functions (CFs), and channel capacities with respect to the different physical features of the RIS and non-stationary properties of the channel model are derived and analyzed. Finally, simulation results are provided to demonstrate that the proposed framework is helpful to achieve a good tradeoff between model complexity and accuracy for investigating the channel propagation characteristics, and therefore providing highly-efficient communications in RIS-enabled UAV-to-vehicle wireless networks.