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Joint Radar-Communication Systems by Optimizing Radar Performance and Quality of Service for Communication Users

  • Christos G. Tsinos
  • , Aakash Arora
  • , Theodoros A. Tsiftsis*
  • *Corresponding author for this work

Research output: Journal PublicationArticlepeer-review

7 Citations (Scopus)

Abstract

In this article, the problem of linear precoding and radar receive beamforming design for joint radar-communication (JRC) systems is studied. A multiple antenna base station (BS) that serves multiple single-antenna user terminals on the downlink is assumed. Furthermore, the BS employs a simultaneous radar function in the form of point-like target detection from the reflected return signals in a signal-dependent interference environment. In this work, we jointly design the JRC linear precoder and the radar receive beamformer, thus aiming to optimize the performance of the radar part while maintaining a desired quality of service (QoS) for the communication one subject to a total transmit power constraint. To that end, we formulate a challenging fractional nonconvex optimization problem via which the optimal precoder and radar receive beamformer are derived. Then, we develop algorithmic solutions based on the majorization-minimization (MM) principle and the semidefinite relaxation (SDR) methodology for the formulated optimization problem. The performance of both the proposed solutions is examined and compared to the one of a system that supports only the radar functionality via numerical results.

Original languageEnglish
Pages (from-to)778-790
Number of pages13
JournalIEEE Transactions on Radar Systems
Volume2
DOIs
Publication statusPublished - 2024

Free Keywords

  • Beamforming
  • Dinkelbach method
  • joint radar-communication (JRC)
  • majorization-minimization (MM)
  • precoding
  • semidefinite programming (SDP)
  • spectrum sharing

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Electrical and Electronic Engineering
  • Signal Processing
  • Control and Systems Engineering
  • Automotive Engineering
  • Artificial Intelligence
  • Atmospheric Science

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