基于AI赋能干扰管理的网络化感知：利用感知移动网络中的宏分集与阵列增益
Networked Sensing with AI-Empowered Interference Management: Exploiting Macro-Diversity and Array Gain in Perceptive Mobile Networks
AI总结：
本文针对感知移动网络中感知与通信干扰及分布式感知节点联合处理问题，提出两阶段干扰管理协议、网络化感知检测器和SN选择算法，并设计分布式模型驱动深度学习算法提升杂波估计效率，仿真验证了网络化感知增益和算法高效性。
摘要：
感知将成为未来无线网络的一项重要服务，以支持自动驾驶和环境监测等创新应用。感知移动网络（PMNs）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的蜂窝结构为感知同一目标提供了多个视角，但感知与通信之间固有的干扰以及分布式感知节点（SNs）之间的联合处理也给PMN的设计带来了巨大挑战。本文首先提出一种两阶段协议来处理两个子系统之间的干扰。具体而言，由通信信号产生的回波（即对感知的干扰）首先在杂波估计（CE）阶段被估计出来，然后在目标感知（TS）阶段被用于干扰管理。随后推导出一种网络化感知检测器，以利用多个SN提供的视角来感知同一目标。研究了来自多个SN的宏分集以及每个SN的多个接收天线带来的阵列增益和更高的角度分辨率，以揭示网络化感知的益处。此外，我们推导出保证一个SN的贡献为正的充分条件，并基于该条件提出了一种SN选择算法。为降低通信工作量，我们提出了一种分布式模型驱动的深度学习算法，利用部分采样数据进行CE。仿真结果证实了网络化感知的益处，并验证了所提出的CE算法相比现有方法具有更高的效率。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
