Synesthesia of Machines Based Multi-Modal Intelligent V2V Channel Model

TL;DR

This paper introduces a 6G multi-modal V2V channel model based on LiDAR data and Synesthesia of Machines, enabling detailed recognition of scatterers and modeling of channel non-stationarity for intelligent vehicular communication.

Contribution

It presents a novel dataset, a neural network-based scatterer recognition algorithm, and a new channel model that captures dynamic environmental interactions in V2V scenarios.

Findings

Simulation results align with ray-tracing data.

The model accurately captures dynamic and static scatterers.

Channel statistical properties are effectively characterized.

Abstract

This paper proposes a novel sixth-generation (6G) multi-modal intelligent vehicle-to-vehicle (V2V) channel model from light detection and ranging (LiDAR) point clouds based on Synesthesia of Machines (SoM). To explore the mapping relationship between physical environment and electromagnetic space, a new V2V high-fidelity mixed sensing-communication integration simulation dataset with different vehicular traffic densities (VTDs) is constructed. Based on the constructed dataset, a novel scatterer recognition (ScaR) algorithm utilizing neural network SegNet is developed to recognize scatterer spatial attributes from LiDAR point clouds via SoM. In the developed ScaR algorithm, the mapping relationship between LiDAR point clouds and scatterers is explored, where the distribution of scatterers is obtained in the form of grid maps. Furthermore, scatterers are distinguished into dynamic and static scatterers based on LiDAR point cloud features, where parameters, e.g., distance, angle, and number, related to scatterers are determined. Through ScaR, dynamic and static scatterers change with the variation of LiDAR point clouds over time, which precisely models channel non-stationarity and consistency under different VTDs. Some important channel statistical properties, such as time-frequency correlation function (TF-CF) and Doppler power spectral density (DPSD), are obtained. Simulation results match well with ray-tracing (RT)-based results, thus demonstrating the necessity of exploring the mapping relationship and the utility of the proposed model.