Predictive Target-to-User Association in Complex Scenarios via Hybrid-Field ISAC Signaling

TL;DR

This paper introduces a robust T2U association framework for V2I networks in hybrid near-field and far-field scenarios, utilizing predictive filtering and probabilistic data association to improve tracking and reliability.

Contribution

It proposes a novel hybrid-field ISAC signaling-based framework with an interacting multiple-model filter and probabilistic data association, addressing complex vehicle maneuvers and ambiguity.

Findings

Significantly improves tracking accuracy over conventional methods.

Enhances association reliability in dense traffic scenarios.

Validates effectiveness through numerical simulations.

Abstract

This paper presents a novel and robust target-to-user (T2U) association framework to support reliable vehicle-to-infrastructure (V2I) networks that potentially operate within the hybrid field (near-field and far-field). To address the challenges posed by complex vehicle maneuvers and user association ambiguity, an interacting multiple-model filtering scheme is developed, which combines coordinated turn and constant velocity models for predictive beamforming. Building upon this foundation, a lightweight association scheme leverages user-specific integrated sensing and communication (ISAC) signaling while employing probabilistic data association to manage clutter measurements in dense traffic. Numerical results validate that the proposed framework significantly outperforms conventional methods in terms of both tracking accuracy and association reliability.