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Near Range, Leakage Aware Object Localization Based on 5G NR-Compliant JCAS

Research output: Chapter in Book/Report/Conference proceedingConference proceedingspeer-review

Abstract

In this work, we investigate joint communications and sensing (JCAS) with a 5G New Radio (NR)-compliant orthogonal frequency-division multiplexing (OFDM) system. The focus lies on the special task of estimating the distances of objects in the close proximity of the antenna from a user equipment (UE) point of view. Unfortunately, in the range spectrum such near range objects may be covered by a leakage signal arising from the own transmitter. The interfering signal is typically much stronger in signal power compared to the objects’ reflections. Thus, for close objects, classical OFDM-based radar processing methods as well as advanced algorithms like multiple signal classification (MUSIC) are in many situations not able to separate the objects and the leakage. In this work, we propose a modified version of the sparse cyclic coordinate decent (SCCD) algorithm by combining it with the so-called RELAX method to separate and estimate the positions of the leakage and the objects. A proof of concept is presented via simulations and measurements, which highlights that the proposed method outperforms state-of-the-art (SOTA) algorithms, particularly in the mid signal-to-noise ratio (SNR) range.
Original languageEnglish
Title of host publicationProceedings of the Asilomar Conference on Signals, Systems, and Computers (ACSSC 2024)
EditorsMichael B. Matthews
PublisherIEEE
Pages32-38
Number of pages7
Edition1
ISBN (Electronic)979-8-3503-5405-8
DOIs
Publication statusPublished - Apr 2025

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)1058-6393

Fields of science

  • 202040 Transmission technology
  • 202 Electrical Engineering, Electronics, Information Engineering
  • 202022 Information technology
  • 202030 Communication engineering
  • 202037 Signal processing
  • 202015 Electronics
  • 202028 Microelectronics
  • 202041 Computer engineering
  • 202036 Sensor systems

JKU Focus areas

  • Digital Transformation

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