Multi-Periodogram Velocity Estimation with Irregular Reference Signals for Robot-Aided ISAC

A new paper from Yi Geng, Pan Cao, Ting Zeng, and Yongqian Deng, accepted at ICC2026, tackles velocity estimation in robot-aided integrated sensing and communications (ISAC). In this setup, mobile robots act as sensing nodes but must opportunistically reuse irregular 5G/6G reference signals (RSs) rather than dedicated sensing pilots. The authors show that the velocity profile from such irregular time-domain patterns can be decomposed into a periodic-peak component and an amplitude-shaping (weighting) component. Building on this insight, they propose a multi-periodogram velocity estimation algorithm that is standard-compliant—no new sensing-dedicated RSs or 3GPP modifications required.

Simulation results demonstrate significant performance gains over conventional periodogram processing. The proposed method achieves a 3 dB SNR gain at the 10% missed-detection rate and reduces false alarms by 51%, notably improving low-SNR robustness. This work is particularly relevant for 5G-Advanced and 6G networks, where integrating sensing and communication functions without additional spectrum or hardware is a key goal. By enabling robots to accurately estimate velocities using existing cellular signals, the approach could accelerate deployment of autonomous robotics in industrial IoT, smart factories, and logistics, where reliable sensing is critical for navigation and collision avoidance.