OTFS for Joint Radar and Communication: Algorithms, Prototypes, and Experiments
Xiaojuan Zhang, Yonghong Zeng, Francois Chin Po Shin
TL;DR
This work addresses the challenge of joint radar and communication in high-mobility environments by leveraging Orthogonal Time Frequency Space (OTFS) signaling within an Integrated Sensing and Communication (ISAC) framework. A fast OTFS radar algorithm with self-interference cancellation (FAOR) is developed to enable robust range and speed estimation, while extending sensing to human vital signs (breathing rate and heartbeat). The authors propose two human-target discrimination approaches—signal-processing based and machine learning-based—and validate them on a hardware prototype built with USRP2944 and mmWave front-ends, demonstrating effective target detection and classification in static and moving scenarios. The results demonstrate practical feasibility and highlight both the advantages and hardware constraints of OTFS-based JRC for real-world deployments and future 6G-based sensing applications.
Abstract
We propose an Joint Radar and Communication (JRC) system that utilizes the Orthogonal Time Frequency Space (OTFS) signals. The system features a fast radar sensing algorithm for detecting target range and speed by using the OTFS communication signals, and a self-interference cancellation for enhanced multi-target separation. In addition to target detection, we propose methods for monitoring human vital signs, such as breathing rate and heartbeat. Furthermore, we explore two approaches for distinguishing between human and nonhuman targets: one based on signal processing and the other based on machine learning. We have developed a prototype JRC system using the software-defined radio (SDR) technology. Experimental results are shown to demonstrate the effectiveness of the prototype in detecting range, speed, and vital signs in both human and mobile robot scenarios, as well as in distinguishing between human and non-human targets.