Research and Development of a Joint Communication and Sensing Test Bed

Abstract

In the recent decade, joint communication and sensing (JCAS) has become a thriving field of research. Perceptive Mobile Networks (PMN) represents an innovative implementation of the JCAS philosophy in cellular mobile networks, aiming to integrate radio sensing with the current “communication-only” cellular mobile networks. This cutting-edge technology enables enhanced functionalities for both purposes and mitigates mutual interference between them, thus leading to extensive applications in the next-generation mobile communication industry. One vital application of this technology is to track moving targets with the current wireless infrastructure.

Uplink sensing offers feasible solutions with minimal adaptations of the existing mobile infrastructure. However, the uplink setup results in inherent clock asynchrony between transmitters and receivers, causing ambiguity in Doppler parameter estimation. Therefore, it is essential to develop new technologies that address asynchrony and provide practical solutions for the instant localization of moving targets. This research contributes to developing an uplink sensing demonstrator that enables real-time tracking of moving human targets with Long Term Evolution-based (LTE-based) signals. This thesis focuses on two research questions.

• To design a robust detection and tracking scheme based on channel state information (CSI). This scheme is expected to achieve sub-meter tracking accuracy with real-time capability, utilizing the multiple-input-multiple-output (MIMO) setup and LTE-based signal structure.

• To develop and implement a real-time demonstration test bed for human target localization.

To address the first challenge, this thesis proposes a well-structured workflow that effectively distinguishes system anomalies from human motion. Moreover, to tackle clock asynchrony, this thesis integrates a CSI ratio-based Doppler frequency estimator with a maximum likelihood estimation-based estimator for angle-of-arrival (AoA) and propagation delay estimation. Based on the above scheme, a target tracking module is developed in Python.

To address the second challenge, an uplink sensing demonstrator is developed using a National Instrument Massive MIMO prototyping test bed and its supporting software, MIMO Framework Application (MFA). A reliable pilot-streaming interface is implemented within MFA, incorporating a pre-processing module to prepare pilot samples and a module to stream the pilot samples via User Datagram Protocol (UDP) datagrams. In addition, a multi-thread Python program is developed to concurrently receive CSI data samples, perform data processing, and display the updated localization on the monitor.

The evaluations verify that the demonstration system enables real-time tracking of a single human target with sub-meter accuracy in various scenarios.