mlsanityData to 7 Oct 2026

Deep Learning-based Target-To-User Association in Integrated Sensing and Communication Systems

arXiv 2401.12801 ↗First posted 11 Jan 2024cs.NI cs.LG eess.SPcited by 0

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 27 of its 52 references that are in the map. First posted on arXiv 11 Jan 2024.

Builds on

27 of 52 references.

References · 27

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  3. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
  4. 2016Feature Pyramid Networks for Object Detectioncited by 5,021
  5. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,829
  6. 2015SSD: Single Shot MultiBox Detectorcited by 3,125
  7. 2015Fast R-CNNcited by 2,930
  8. 2013Rich feature hierarchies for accurate object detection and semantic segmentationcited by 2,160
  9. 2019MMDetection: Open MMLab Detection Toolbox and Benchmarkcited by 1,379
  10. 2022YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectorscited by 978
  11. 2018Path Aggregation Network for Instance Segmentationcited by 796
  12. 2014Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognitioncited by 719
15 more
  1. 2019Object Detection in 20 Years: A Surveycited by 528
  2. 2019Distance-IoU Loss: Faster and Better Learning for Bounding Box Regressioncited by 454
  3. 2019CSPNet: A New Backbone that can Enhance Learning Capability of CNNcited by 381
  4. 2020Inductive Biases for Deep Learning of Higher-Level Cognitioncited by 273
  5. 2021TOOD: Task-aligned One-stage Object Detectioncited by 218
  6. 2021Integrated Sensing and Communications: Towards Dual-functional Wireless Networks for 6G and Beyondcited by 88
  7. 2019Deep Learning for mmWave Beam and Blockage Prediction Using Sub-6GHz Channelscited by 41
  8. 2013Millimeter Wave Channel Modeling and Cellular Capacity Evaluationcited by 29
  9. 2021An Overview of Signal Processing Techniques for Joint Communication and Radar Sensingcited by 19
  10. 2021Radar Aided 6G Beam Prediction: Deep Learning Algorithms and Real-World Demonstrationcited by 17
  11. 2021Vehicular Blockage Modelling and Performance Analysis for mmWave V2V Communicationscited by 5
  12. 2021Channel Estimation for 6G V2X HybridSystems using Multi-Vehicular Learningcited by 2
  13. 2021Fastening the Initial Access in 5G NR Sidelink for 6G V2X Networkscited by 1
  14. 2021BeamSurfer: Minimalist Beam Management of Mobile mm-Wave Devicescited by 1
  15. 2023Target-to-User Association in ISAC Systems With Vehicle-Lodged RIScited by 1

Built on by

Not cited yet.