Current architecture proposals within standards development organizations such as ETSI and 3GPP enable sensing capabilities in mobile networks; however, they do not include a repository for storing sensing data. Such a repository can be used for AI model training and to complement ongoing sensing service provisioning by improving efficiency and accuracy. One way of realizing this is through the fusion of historical sensing data with live sensing data. In this paper, we study historical and live sensing data fusion for Integrated Sensing and Communication in future 6G systems and introduce a Sensing Data Storage Function to store historical sensing data and sensing results. We show how the Sensing Data Storage Function can be used with other network functions in a 6G architecture proposition for Integrated Sensing and Communication. We validate our proposal with a measurement model and show performance improvements in terms of detection probability and false-alarm rate. The network functionality to fuse and process sensing data combines live sensing measurements with previously sensed historical sensing data using a map-aware hard filter that rejects detections consistent with known static structures. Our simulation illustrates that, for a traffic junction scenario, map-aware hard filtering substantially reduces false alarms without degrading detection probability.

翻译：当前标准开发组织（如ETSI和3GPP）内的架构提案已支持移动网络中的感知能力，但尚未包含用于存储传感数据的存储库。此类存储库可用于AI模型训练，并通过提升效率与精度来补充持续进行的感知服务提供。实现该目标的一种途径是将历史传感数据与实时传感数据融合。本文研究了面向未来6G系统内集成感知与通信的历史与实时传感数据融合，并引入了一种传感数据存储功能，用于存储历史传感数据及感知结果。我们展示了在6G集成感知与通信架构提案中，传感数据存储功能如何与其他网络功能协同使用。通过测量模型验证了我们的方案，并展示了在检测概率与虚警率方面的性能提升。该融合与处理传感数据的网络功能将实时感知测量值与先前感知的历史传感数据结合，使用一种地图感知硬滤波器来剔除与已知静态结构一致的检测结果。仿真结果表明，在交通路口场景下，地图感知硬滤波在不降低检测概率的前提下显著减少了虚警。