Sensing using cellular infrastructure may be one of the defining feature of sixth generation (6G) wireless systems. Wideband 6G communication channels operating at higher frequency bands (upper mmWave bands) are better modeled using clustered geometric channel models. In this paper, we propose methods for detection of passive targets and estimating their position using communication deployment without any assistance from the target. A novel AI architecture called CsiSenseNet is developed for this purpose. We analyze the resolution, coverage and position uncertainty for practical indoor deployments. Using the proposed method, we show that human sized target can be sensed with high accuracy and sub-meter positioning errors in a practical indoor deployment scenario.

翻译：随着通信基础设施的普及，基于移动通信网络的感应技术正在成为第六代（6G）无线系统的显著特征。基于上 mmWave 频段的宽带通信渠道可使用集群几何通道模型更好地建模。本文提出了一种利用通信部署进行被动目标检测和位置估计的方法，无需目标附加任何设备。我们首次研发了一种名为CsiSenseNet的AI架构，用于此目的。我们分析了实际室内部署的分辨率、覆盖范围和位置不确定性。利用所提出的方法，我们展示了在实际室内部署场景中，可高精度感测到人体大小的被动目标，并实现亚米级定位误差。