论文标题
无线通信系统中的杂种主动和被动感应
Hybrid Active and Passive Sensing for SLAM in Wireless Communication Systems
论文作者
论文摘要
将传感功能集成到未来的移动设备中已成为一个重要趋势。在现有的通信硬件体系结构下实现不同类型的感应和实现相互增强,这是实现感应和交流的深入整合的关键挑战。在5G新的无线电上下文中,可以通过在用户设备(UE)侧扫描的上行链路光束来观察周围环境来执行主动传感。此外,UE可以通过下行链路通道估计进行被动传感,以测量多径成分(MPC)信息。这项研究是第一个开发杂交同时定位和映射(SLAM)机制的研究,该机制结合了主动和被动的感应,其中在通信系统中实现了两种传感模式之间的相互增强。具体而言,我们首先建立了与反射表面相关的共同特征,以桥梁主动和被动传感,从而实现信息融合。基于共同特征,我们可以在主动感测的帮助下通过MPC实现物理锚定初始化。然后,我们扩展了经典的概率数据关联猛击机制,以实现UE定位,并通过随后的被动传感不断地完善物理锚和目标反射。数值结果表明,提出的基于被动传感的混合动力和被动传感的SLAM机制可以在棘手的情况下成功起作用,而无需有关平面图,锚或代理的任何事先信息。此外,与仅主动感应机制相比,提出的算法表现出显着的性能增长。
Integrating sensing functions into future mobile equipment has become an important trend. Realizing different types of sensing and achieving mutual enhancement under the existing communication hardware architecture is a crucial challenge in realizing the deep integration of sensing and communication. In the 5G New Radio context, active sensing can be performed through uplink beam sweeping on the user equipment (UE) side to observe the surrounding environment. In addition, the UE can perform passive sensing through downlink channel estimation to measure the multipath component (MPC) information. This study is the first to develop a hybrid simultaneous localization and mapping (SLAM) mechanism that combines active and passive sensing, in which mutual enhancement between the two sensing modes is realized in communication systems. Specifically, we first establish a common feature associated with the reflective surface to bridge active and passive sensing, thus enabling information fusion. Based on the common feature, we can attain physical anchor initialization through MPC with the assistance of active sensing. Then, we extend the classic probabilistic data association SLAM mechanism to achieve UE localization and continuously refine the physical anchor and target reflections through the subsequent passive sensing. Numerical results show that the proposed hybrid active and passive sensing-based SLAM mechanism can work successfully in tricky scenarios without any prior information on the floor plan, anchors, or agents. Moreover, the proposed algorithm demonstrates significant performance gains compared with active or passive sensing only mechanisms.