Near-Field Integrated Sensing, Computing and Semantic Communication in Digital Twin-Assisted Vehicular Networks

2026-04-07

AI Summary

This work addresses the challenges of massive data volume, stringent perception accuracy requirements, and limited computational resources in digital twin-enabled vehicular networks operating in dynamic environments. To this end, the paper proposes an integrated near-field sensing–computation–semantic communication framework that, for the first time, deeply fuses semantic communication with near-field sensing. The approach leverages millimeter-wave radar mapping from roadside units combined with particle filtering to achieve high-precision vehicle tracking. Furthermore, it employs a multi-user MIMO architecture, a hybrid heuristic-based vehicle assignment scheme, and an alternating optimization of semantic extraction ratios and beamforming matrices to jointly enhance communication efficiency and sensing performance under constrained resources. Experimental results demonstrate that the proposed framework improves semantic transmission rates by 20% over existing methods while maintaining comparable perception accuracy.

Abstract

Digital twin (DT) technology offers transformative potential for vehicular networks, enabling high-fidelity virtual representations for enhanced safety and automation. However, seamless DT synchronization in dynamic environments faces challenges such as massive data transmission, precision sensing, and strict computational constraints. This paper proposes an integrated sensing, computing, and semantic communication (ISCSC) framework tailored for DT-assisted vehicular networks in the near-field (NF) regime. Leveraging a multi-user multiple-input multiple-output (MU-MIMO) configuration, each roadside unit (RSU) employs semantic communication to serve vehicles while simultaneously utilizing millimeter-wave (mmWave) radar for environmental mapping. We implement particle filtering at RSUs to achieve high-precision vehicle tracking. To optimize performance, we formulate a joint optimization problem balancing semantic communication rates and sensing accuracy under limited computational resources and power budget. Our solution includes a hybrid heuristic algorithm for vehicle-to-RSU assignment and an alternating optimization approach for determining semantic extraction ratios and beamforming matrices. Performance is extensively evaluated via the Cramér-Rao bound (CRB) for angle and distance estimation, semantic transmission rates, and resource utilization. Numerical results demonstrate that the proposed ISCSC framework achieves a 20% improvement in transmission rate while maintaining the sensing accuracy of existing integrated sensing and communication (ISAC) schemes under constrained resource conditions.

Problem

• Digital Twin
• Vehicular Networks
• Near-Field
• Integrated Sensing and Communication
• Semantic Communication

Innovation

• Integrated Sensing and Communication
• Semantic Communication
• Digital Twin
• Near-Field
• MU-MIMO