Enabling ISAC in Real World: Beam-Based User Identification with Machine Learning

Leveraging perception from radar data can assist multiple communication tasks, especially in highly-mobile and large-scale MIMO systems. One particular challenge, however, is how to distinguish the communication user (object) from the other mobile objects in the sensing scene. This paper formulates this $\mathit{\text{user}}\mathit{\text{identification}}$ problem and develops two solutions, a baseline model-based solution that maps the objects angles from the radar scene to communication beams and a scalable deep learning solution that is agnostic to the number of candidate objects. Using the DeepSense 6G dataset, which have real-world measurements, the developed deep learning approach achieves more than $93.4\mathrm{\%}$ communication user identification accuracy, highlighting a promising path for enabling integrated radar-communication applications in the real world.