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arXiv · 2411.06578

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

Umut Demirhan·Ahmed Alkhateeb
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Abstract

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 \textit{user 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 $93.4\%$ communication user identification accuracy, highlighting a promising path for enabling integrated radar-communication applications in the real world.

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Umut Demirhan, Ahmed Alkhateeb. 2024-11-10. Enabling ISAC in Real World: Beam-Based User Identification with Machine Learning. https://arxiv.org/abs/2411.06578

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