Sensing will be an important service for future wireless networks to assist innovative applications like 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 and the joint processing among distributed sensing nodes (SNs) 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, which are 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 and the array gain from multiple receive antennas at each SN are investigated to reveal the benefit of networked sensing. Furthermore, we derive the sufficient condition that one SN's contribution to networked sensing is positive, based on which a SN selection algorithm is proposed. To reduce the computation and communication workload, we propose a 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，以支持自动驾驶和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的蜂窝结构提供了多个视角来感知同一目标，但感知与通信之间的固有干扰以及分布式感知节点（SN）间的联合处理给PMN设计带来了巨大挑战。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问题。具体而言，由通信信号产生的回波（即对感知的干扰）先在杂波估计（CE）阶段被估计，随后在目标感知（TS）阶段用于干扰管理。接着，推导出一种网络化感知检测器，以利用多个SN提供的视角对同一目标进行感知。研究了多个SN带来的宏观分集以及每个SN的多接收天线带来的阵列增益，揭示了网络化感知的益处。此外，推导了单个SN对网络化感知贡献为正的充分条件，并据此提出一种SN选择算法。为降低计算和通信负载，提出一种利用部分采样数据进行CE的模型驱动深度学习算法。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效率。