Full-Duplex V2X Integrated Sensing and Communication Scenario: Stochastic geometry, Monte-Carlo, and Ray-Tracing Comparison

TL;DR

This paper compares stochastic geometry, Monte-Carlo, and ray-tracing methods to evaluate integrated sensing and communication performance in V2X scenarios, highlighting the accuracy and discrepancies among these frameworks.

Contribution

It introduces a comprehensive comparison of three modeling frameworks for ISAC V2X systems, with parameters derived from ray-tracing simulations.

Findings

SG and MC models are accurate for univariate metrics

Discrepancies are larger for joint sensing and communication metrics

RT-based parameters improve model relevance

Abstract

In this paper, performance of an Integrated Sensing and Communication (ISAC) Vehicle-to-Everything (V2X) scenario is evaluated, in which a vehicle simultaneously detects the next vehicle ahead while receiving a communication signal from a RoadSide Unit (RSU) of the infrastructure. Univariate and joint radar and communication performance metrics are evaluated within three different frameworks, namely the Stochastic Geometry (SG), Monte-Carlo (MC), and Ray-Tracing (RT) frameworks. The parameters of the system model are extracted from the RT simulations, and the metrics are compared to assess the accuracy of the SG framework. It is shown that the SG and MC system models are relevant w.r.t. RT simulations for the evaluation of univariate communication and sensing metrics, but larger discrepancies are observed for the joint metrics.