Chirp Delay-Doppler Domain Modulation Based Joint Communication and Radar for Autonomous Vehicles

2025-12-16

AI Summary

To address the performance degradation in intelligent vehicle cooperation caused by the decoupling of communication and sensing functions under dynamic scenarios, this paper proposes a perception-centric millimeter-wave integrated sensing and communication (ISAC) architecture. We innovatively design a chirp-based QAM modulation scheme in the delay-Doppler-amplitude domain, enabling simultaneous high-precision radar detection and robust communication within a single waveform. We further establish, for the first time, a four-dimensional parameter estimation model in the delay-Doppler domain—estimating range, radial velocity, orientation angle, and tangential velocity—thereby extending beyond conventional 4D radar capabilities. Additionally, we introduce a dual-compensation demodulation and tracking mechanism, allowing passive vehicles to demodulate communication signals losslessly while maintaining centimeter-level ranging accuracy (±1 cm) and 0.1° orientation resolution. Simulation results demonstrate significantly enhanced communication throughput and synergistic improvement in ISAC performance under dynamic conditions.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Multi-modal Vision
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

This paper introduces a sensing-centric joint communication and millimeter-wave radar paradigm to facilitate collaboration among intelligent vehicles. We first propose a chirp waveform-based delay-Doppler quadrature amplitude modulation (DD-QAM) that modulates data across delay, Doppler, and amplitude dimensions. Building upon this modulation scheme, we derive its achievable rate to quantify the communication performance. We then introduce an extended Kalman filter-based scheme for four-dimensional (4D) parameter estimation in dynamic environments, enabling the active vehicles to accurately estimate orientation and tangential-velocity beyond traditional 4D radar systems. Furthermore, in terms of communication, we propose a dual-compensation-based demodulation and tracking scheme that allows the passive vehicles to effectively demodulate data without compromising their sensing functions. Simulation results underscore the feasibility and superior performance of our proposed methods, marking a significant advancement in the field of autonomous vehicles. Simulation codes are provided to reproduce the results in this paper: href{https://github.com/LiZhuoRan0/2026-IEEE-TWC-ChirpDelayDopplerModulationISAC}{https://github.com/LiZhuoRan0}.

Problem

• Develops a joint communication and radar system for autonomous vehicles
• Proposes a modulation scheme for data transmission in delay-Doppler domain
• Enables accurate 4D parameter estimation in dynamic environments

Innovation

• Chirp waveform-based delay-Doppler QAM modulation
• Extended Kalman filter for 4D parameter estimation
• Dual-compensation demodulation and tracking scheme