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 achievable performance of a 3GPP network by combining the measurements obtained independently at different TRP. Extensive evaluations demonstrate that multi-TRP assistance improves target observability, reduces spurious detections, and tightens localization error distributions at the cost of additional sensing overhead due to the need for multiple TRPs to periodically allocate radio resources for sensing measurements. In the evaluated scenario, results show that a voting threshold of two assisting TRPs achieves an optimal trade-off between miss detection probability and false alarm suppression, meeting 3GPP performance objectives. Furthermore, we quantify the sensing overhead and show that proper system design, tuned to the application requirements, can substantially reduce its impact: for example, extending the sensing refresh interval beyond the 128 ms coherent processing interval to 1 s reduces the effective overhead from 29 % to approximately 3.7 %, enabling more scalable network deployment.

翻译：感知与通信一体化（ISAC）目前正在3GPP新空口（NR）框架内进行标准化，旨在使蜂窝基础设施能够利用现有通信波形执行感知功能。尽管标准化工作持续推进，实际部署仍可能受限于与场景相关的可观测性约束。例如，在UMa-AV场景中，单TRP感知可能因角覆盖范围受限、部分遮挡以及视场有限而受到影响，从而降低三维无人机环境中的检测可靠性。为此，多TRP方案被提出以提升空间分集和感知鲁棒性。本文在标准化3GPP UMa-AV信道假设和Release 19评估参数下，对多TRP辅助的单站感知用于无人机检测进行了系统级研究。我们提出了一种空间分集融合框架，并通过结合不同TRP独立获得的测量结果，评估了3GPP网络的可行性能。大量评估表明，多TRP辅助可提升目标可观测性，减少虚警检测，并收紧定位误差分布，但代价是需多个TRP周期性分配无线资源进行感知测量，从而增加额外感知开销。在所评估场景中，两个辅助TRP的投票阈值可在漏检概率与虚警抑制之间实现最优权衡，满足3GPP性能目标。此外，我们量化了感知开销，并表明根据应用需求适当设计系统可显著降低其影响：例如，将感知刷新间隔从128毫秒相干处理间隔延长至1秒，可将有效开销从29%降至约3.7%，从而实现更可扩展的网络部署。