Enabling mm-Wave 5G Joint Communication and AI-based Sensing for Urban Situational Awareness

Abstract

We present an antennas-to-AI platform for joint communication and sensing. It leverages the hardware and processing required for standard mm-Wave 5G communications to perform sensing tasks. It's key capabilities and metrics include (i) synchronization of I/Q data (up to 200~MSPS) with beam steering (among 9601 beams) with 10~ns accuracy; (ii) a signal processing pipeline that extracts communication features such as the SNR and channel response from received 5G waveforms; and (iii) system orchestration that synchronizes the receiver (RX) to the 5G frame structure of the base station (gNodeB) and maintains it within a worst-case OFDM cyclic prefix of 0.29~$\mu$s. The platform is also able to emulate gNodeB transmissions. We demonstrate AI-based object classification only using the directional communication features derived by the platform from ambient 5G signals transmitted by a gNodeB. Six objects are classified with 98% accuracy in an indoor environment.