Hybrid Far- and Near-Field Modeling for Reconfigurable Intelligent Surface Assisted V2V Channels: A Sub-Array Partition Based Approach

Abstract

Reconfigurable intelligent surface (RIS)-assisted communications has been a hot topic due to its promising advantages for future wireless networks. Existing works on RIS-assisted channel modeling have mainly focused on far-field propagation condition with planar wavefront assumption. In essence, the far-field condition does not always hold because the RIS array dimension may be comparable to the terminal distance, especially in RIS-assisted mobile networks. To this end, we propose a hybrid far- and near-field stochastic channel model for characterizing a RIS-assisted vehicle-to-vehicle (V2V) propagation environment, which takes into account both far-field and near-field propagation conditions. To achieve the balance between the modeling accuracy and complexity for the investigation of the RIS-assisted V2V propagation characteristics, we develop a sub-array partitioning scheme to dynamically divide the entire RIS array into several smaller sub-arrays, which makes planar wavefront assumption applicable for the sub-arrays. Important channel statistical properties, including spatial cross-correlation functions (CCFs), temporal auto-correlation functions (ACFs), and frequency correlation functions (FCFs), are derived and investigated. Simulation results are provided to show the performance of the proposed sub-array partition based hybrid far- and near-field modeling solution for RIS-assisted V2V channels.