Optimal Joint Radar and Communications Beamforming for the Low-Altitude Airborne Vehicles in SAGIN

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Abstract

A symbolic feature that integrates the space, air and ground network components for service in challenging and remote areas is being envisaged with continuity and high mobility of the 6G mobile system. Simultaneously providing sensing and connectivity over the radio signal becomes essential to support the management of low-space air crafts in the mobile system with limited spectrum resources. In this paper, we investigate the optimal joint radar and communications beamforming scheme with the presence of the clutter to support the low-space airborne vehicles, e.g. unmanned aerial vehicles or drones that are essential components of Non-Terrestrial Networks. The proposed scheme achieves the optimal signal-to-clutter-plus-noise ratio of the sensing function while maintaining the performance of the predefined communications. The novel application of approximations and rank-reduction algorithms in this work maximizes the joint radar and communications performance, for a system model similar to the one that is solved with a local optimum solution in a previous work. The numeric simulation results show that our approach maintains low complexity while guaranteeing the global optimum beamforming solution.
Original languageEnglish
Title of host publication2023 IEEE Wireless Communications and Networking Conference, WCNC 2023
PublisherIEEE
Number of pages5
ISBN (Electronic)978-1-6654-9122-8
ISBN (Print)978-1-6654-9123-5
DOIs
Publication statusPublished - 2023
Publication typeA4 Article in conference proceedings
EventIEEE Wireless Communications and Networking Conference - Glasgow, United Kingdom
Duration: 26 Mar 202329 Mar 2023

Publication series

NameIEEE Wireless Communications and Networking Conference, WCNC
ISSN (Print)1525-3511
ISSN (Electronic)1558-2612

Conference

ConferenceIEEE Wireless Communications and Networking Conference
Country/TerritoryUnited Kingdom
CityGlasgow
Period26/03/2329/03/23

Publication forum classification

  • Publication forum level 1

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