Deep Learning-based Target-To-User Association in Integrated Sensing and
  Communication Systems
Lorenzo Cazzella Marouan Mizmizi Dario Tagliaferri Damiano Badini Matteo Matteucci Umberto Spagnolini
In Integrated Sensing and Communication (ISAC) systems, matching the radar
targets with communication user equipments (UEs) is functional to several
communication tasks, such as proactive handover and beam prediction. In this
paper, we consider a radar-assisted communication system where a base station
(BS) is equipped with a multiple-input-multiple-output (MIMO) radar that has a
double aim: (i) associate vehicular radar targets to vehicular equipments (VEs)
in the communication beamspace and (ii) predict the beamforming vector for each
VE from radar data. The proposed target-to-user (T2U) association consists of
two stages. First, vehicular radar targets are detected from range-angle
images, and, for each, a beamforming vector is estimated. Then, the inferred
per-target beamforming vectors are matched with the ones utilized at the BS for
communication to perform target-to-user (T2U) association. Joint multi-target
detection and beam inference is obtained by modifying the you only look once
(YOLO) model, which is trained over simulated range-angle radar images.
Simulation results over different urban vehicular mobility scenarios show that
the proposed T2U method provides a probability of correct association that
increases with the size of the BS antenna array, highlighting the respective
increase of the separability of the VEs in the beamspace. Moreover, we show
that the modified YOLO architecture can effectively perform both beam
prediction and radar target detection, with similar performance in mean average
precision on the latter over different antenna array sizes.