面向网络级集成感知与通信（ISAC）系统的几何感知资源分配
Geometry-Aware Resource Allocation for Network-Level ISAC Systems
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中文总结 AI 辅助
本文针对网络级ISAC系统中空间几何影响被忽略的资源分配挑战，提出两阶段理论框架与VGPA算法，推导最优孔径分布并联合优化通信感知资源。
中文摘要 AI 辅助
网络级集成感知与通信（ISAC）被公认为下一代移动无线电系统的变革性技术，通过允许多个收发信机协同，网络级ISAC可利用空间分集显著提升通信与感知性能。然而，现有资源分配策略通常忽略空间几何的影响：相同的时频资源对感知精度的贡献因收发信机位置不同而存在差异，这使得空间拓扑与资源效能之间的基本耦合关系尚不明确，导致最优资源分配成为释放网络级ISAC全部潜力的关键挑战。为应对该挑战，本文通过理论支撑的两阶段框架研究时频资源在空间分布收发信机间的最优分配。首先，在两发射机与多发射机场景下，解析推导感知所需的最优时间孔径与频率孔径分布，分别定义为已分配符号与子载波索引的方差；利用时延与多普勒估计间的数学同构性，证明最优资源分配策略遵循克拉美罗下界（CRLB）关于孔径的梯度方向。其次，为弥合理论孔径值与实际正交频分多址（OFDMA）约束（如每个用户设备（UE）的最小通信速率）之间的差距，将资源分配建模为组合整数划分问题；针对该问题的NP难特性，提出低复杂度的方差引导划分算法（VGPA），以联合优化通信与感知的子载波及符号模式。
英文摘要
Network-level integrated sensing and communication (ISAC) is recognized as a transformative technology for next-generation mobile radio systems. By enabling collaboration among multiple transceivers, network-level ISAC can significantly enhance both communication and sensing performance through spatial diversity. However, existing resource allocation strategies typically overlook the impact of spatial geometry, where identical time-frequency resources contribute differently to sensing accuracy depending on the transceiver's location. This leaves the fundamental coupling between spatial topology and resource efficacy unclear, rendering optimal resource allocation a critical challenge for unlocking the full potential of network-level ISAC.To address this challenge, this paper investigates the optimal distribution of time-frequency resources across spatially distributed transceivers through a theoretically grounded two-stage framework. First, we analytically derive the optimal time and frequency aperture distributions for sensing, defined as the variances of the allocated symbol and subcarrier indices, respectively, under both two-transmitter and multi-transmitter scenarios. By exploiting the mathematical isomorphism between delay and Doppler estimation, we prove that the optimal resource allocation strategy follows the gradient direction of the Cramer-Rao Lower Bound (CRLB) with respect to the apertures. Second, to bridge the gap between theoretical aperture values and practical OFDMA constraints, such as the minimized communication rate of each user equipment (UE), we formulate the resource allocation as a combinatorial integer partitioning problem. To tackle the NP-hard nature of the formulated problem, a low-complexity Variance-Guided Partitioning Algorithm (VGPA) is proposed to jointly optimize the subcarrier and symbol patterns for communication and sensing.
