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Perceptive Mobile Networks: Cellular Networks With Radio Vision via Joint Communication and Radar Sensing

Open access
Zhang, Andrew, Rahman, Md. Lushanur, Huang, Xiaojing, Guo, Yingjie Jay, Chen, Shanzhi, Heath, Robert W.·2021·IEEE Vehicular Technology Magazine

Abstract

Joint communication and radar/radio sensing (JCAS), also known as dual-function radar communications, enables the integration of communication and radio sensing into one system, sharing a single transmitted signal. The perceptive mobile network (PMN) is a natural evolution of JCAS from simple point-to-point links to a mobile/cellular network with integrated radio-sensing capability. In this article, we present a system architecture that unifies three types of sensing, investigate the required modifications to existing mobile networks, and exemplify the signals applicable to sensing. We also provide a review to stimulate research problems and potential solutions, including mutual information, joint design and optimization for waveform and antenna grouping, clutter suppression, sensing parameter estimation and pattern recognition, and networked sensing under the cellular topology.

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Metadata

Authors
Zhang, A., Rahman, M. L., Huang, X., Guo, Y. J., Chen, S., & Heath, R. W.
Venue
IEEE Vehicular Technology Magazine
Published
2021-06
Volume
16
Issue
2
Pages
20-30
ISSN
  • 1556-6072 (ISSN-L)
  • 1556-6080
OpenAlex
W3111271387
Cited by
279
References
0
FWCI
14.72
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Perceptive Mobile Networks: Cellular Networks With Radio Vision via Joint Communication and Radar Sensing. https://app.cochat.ai/papers/10.1109/mvt.2020.3037430
The paper
Zhang, A., Rahman, M. L., Huang, X., Guo, Y. J., Chen, S., & Heath, R. W. (2021). Perceptive Mobile Networks: Cellular Networks With Radio Vision via Joint Communication and Radar Sensing. IEEE Vehicular Technology Magazine. https://doi.org/10.1109/mvt.2020.3037430

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.