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：利用蜂窝基础设施进行感知可能是第六代（6G）无线系统的标志性特征之一。工作在更高频段（毫米波高频段）的宽带6G通信信道更适合采用聚类几何信道模型进行建模。本文提出了在无需目标协助的情况下，利用通信部署实现无源目标检测与位置估计的方法。为此，我们开发了一种名为CsiSenseNet的新型人工智能架构。针对实际室内部署场景，我们分析了分辨率、覆盖范围及位置不确定性。实验表明，采用所提出的方法可在实际室内部署场景中以高精度和亚米级定位误差实现对人体尺寸目标的感知。