Sensing will be an important service of future wireless networks to assist innovative applications such as autonomous driving and environment monitoring. Perceptive mobile networks (PMNs) were proposed to add sensing capability to current cellular networks. Different from traditional radar, the cellular structure of PMNs offers multiple perspectives to sense the same target, but the inherent interference between sensing and communication along with the joint processing among distributed sensing nodes (SNs) also cause big challenges for the design of PMNs. In this paper, we first propose a two-stage protocol to tackle the interference between two sub-systems. Specifically, the echoes created by communication signals, i.e., interference for sensing, are first estimated in the clutter estimation (CE) stage and then utilized for interference management in the target sensing (TS) stage. A networked sensing detector is then derived to exploit the perspectives provided by multiple SNs for sensing the same target. The macro-diversity from multiple SNs together with the array gain and the higher angular resolution from multiple receive antennas of each SN are investigated to reveal the benefit of networked sensing. Furthermore, we derive the sufficient condition to guarantee one SN's contribution is positive, based on which a SN selection algorithm is proposed. To reduce the communication workload, we propose a distributed model-driven deep-learning algorithm that utilizes partially-sampled data for CE. Simulation results confirm the benefits of networked sensing and validate the higher efficiency of the proposed CE algorithm than existing methods.

翻译：感知将成为未来无线网络的重要服务，以支持自动驾驶与环境监测等创新应用。感知移动网络（PMN）被提出用于为现有蜂窝网络增加感知能力。与传统雷达不同，PMN的蜂窝结构提供了从多个视角感知同一目标的可能性，但感知与通信之间的固有干扰，以及分布式感知节点（SN）间的联合处理，也给PMN设计带来了重大挑战。本文首先提出一种两阶段协议来解决两个子系统间的干扰。具体而言，由通信信号产生的回波（即感知的干扰）首先在杂波估计（CE）阶段进行估计，随后在目标感知（TS）阶段用于干扰管理。随后推导出一种网络化感知检测器，以利用多个SN提供的视角感知同一目标。本文研究了多SN带来的宏分集、阵列增益以及每个SN多接收天线带来的更高角度分辨率，揭示了网络化感知的优势。此外，我们推导了保证单个SN贡献为正的充分条件，并据此提出了一种SN选择算法。为降低通信负载，我们提出了一种基于数据驱动的分布式深度学习算法，该算法利用部分采样数据进行CE。仿真结果证实了网络化感知的优势，并验证了所提CE算法相比现有方法具有更高的效率。