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

TL;DR

The paper tackles enabling simultaneous communication and sensing for autonomous vehicles using a chirp-based Delay-Doppler Quadrature Amplitude Modulation (DD-QAM). It develops a beacon frame aided 4D parameter estimation method to robustly track delay, Doppler, and angles, and extends to a 5D EKF-based tracking approach that additionally estimates tangential velocity and orientation for active vehicles. A dual-compensation demodulation scheme allows passive vehicles to recover data without impairing sensing, supported by theoretical achievable-rate analysis and extensive simulations. Results demonstrate reliable detection, off-grid parameter estimation, and meaningful rate gains, highlighting DD-QAM as a practical JCR design for mmWave autonomous-vehicle scenarios. The open-source simulation code further facilitates reproducibility and future exploration in AV JCR 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}.

Figures (11)

• Figure 1: Organization of this paper.
• Figure 2: TF diagram used to illustrate the relationships among parameters and how data can be modulated in delay: (a) basic dechirp process; (b) data modulation and demodulation process, wherein the data is modulated in delay. The black chirp represents transmitted (Tx) one and the gray chirp represents the received (Rx) one.
• Figure 3: Schematic diagram of the transmit and receive array architecture.
• Figure 4: Schematic diagram illustrating the data modulation scheme of DD-QAM: (a) beacon frame; (b) DDM frame. This RDM is presented from the perspective of the AV or PV. It depicts two transmit antennas and one target in a receive antenna via DDM waveform orthogonality. It shows the way to modulate data in the variation of distance and velocity. The data modulated in complex amplitude is hidden in the complex amplitude of the detected position.
• Figure 5: Geometric relationship and key parameters of the relative motion of the AV and the PV from the perspective of AV. For simplicity and clarity, other targets are not depicted.

Theorems & Definitions (3)

• Remark 1
• Remark 2
• Remark 3