Multi-TRP Assisted UAV Detection in 3GPP 5G-Advanced ISAC Network
Neeraj Varshney, Steve Blandino, Jian Wang, Anuraag Bodi, Camillo Gentile, and Nada Golmie

TL;DR
This paper investigates multi-TRP assisted UAV detection in 3GPP 5G-Advanced ISAC networks, demonstrating improved detection reliability and localization accuracy through spatial diversity fusion, with manageable sensing overhead.
Contribution
It introduces a spatial diversity fusion framework for multi-TRP UAV sensing and evaluates its performance under 3GPP standards, highlighting optimal detection strategies and overhead reduction techniques.
Findings
Multi-TRP assistance enhances target observability and reduces false alarms.
A voting threshold of two TRPs balances miss detection and false alarms.
Adjusting sensing refresh intervals significantly reduces sensing overhead.
Abstract
ISAC is currently being standardized within the 3GPP New Radio (NR) to enable cellular infrastructure to perform sensing using existing communication waveforms. While standardization is progressing, practical deployment may be limited by scenario-dependent observability constraints. For example, in UMa-AV scenarios, sensing with a single TRP can be affected by restricted angular coverage, partial blockage, and limited field of view, which may degrade detection reliability in three-dimensional UAV environments. For this reason, multi-TRP solutions have been suggested to improve spatial diversity and sensing robustness. In this paper, we present a system-level investigation of multi-TRP assisted monostatic sensing for UAV detection under standardized 3GPP UMa-AV channel assumptions and Release 19 evaluation parameters. We propose a spatial diversity fusion framework and evaluate the…
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