arXiv 2604.265522026-04-29eess.SP

协作OFDM-ISAC网络：性能分析与资源分配

Cooperative OFDM-ISAC Networks: Performance Analysis and Resource Allocation

Dalian University of Technology（大连理工大学）；Friedrich-Alexander-University Erlangen-Nuremberg (FAU)（埃尔朗根-纽伦堡弗里德里希·亚历山大大学）

Shoushuo Zhang, Rang Liu, Qian Liu, Ming Li

备注：: submitted to IEEE

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AI总结：

本文研究多基站协作OFDM-ISAC网络的性能分析与时频资源分配，提出信号级和参数级融合架构，推导CRB并开发两阶段CRB-like指标，解决联合RE选择与功率分配问题。

摘要：

基于正交频分复用（OFDM）的协作集成感知与通信（ISAC）利用多基站的空间多样性实现网络级感知。本文研究多基站协作OFDM-ISAC网络的性能分析与时频资源分配，考虑信号级融合（SLF）和参数级融合（PLF）两种架构。对于SLF，推导了目标位置和速度联合估计的CRB。对于PLF，开发了结合本地延迟/多普勒不确定性表征与一阶几何误差传播的两阶段CRB-like指标，并证明只有在限制条件下，Oracle ML基线PLF才能渐近达到SLF的CRB。基于这些结果，提出了在网络级RE独占、每基站功率预算、通信总速率约束和延迟-多普勒模糊函数旁瓣幅度约束下的联合RE选择与功率分配问题。通过Schur补改写和惩罚交替优化开发了高效解决方案。数值结果验证了分析，展示了有效的模糊-旁瓣抑制和一致的定位/速度增益，同时揭示了几何依赖的SLF-PLF性能差距。

英文摘要：

Cooperative integrated sensing and communication (ISAC) based on orthogonal frequency-division multiplexing (OFDM) enables network-wide sensing by exploiting the spatial diversity of multi-base-station (BS). This paper studies performance analysis and time-frequency resource allocation for a multi-BS cooperative OFDM-ISAC network with fine-grained resource-element (RE)-level orthogonal coordination. Two fusion architectures are considered: signal-level fusion (SLF), which forwards raw echoes to a fusion center, and parameter-level fusion (PLF), which reports only local delay/Doppler estimates and their uncertainty information. For SLF, we derive the Cramér--Rao bound (CRB) for joint target position and velocity estimation. For PLF, we develop a two-stage CRB-like metric by combining local delay/Doppler uncertainty characterization with first-order geometric error propagation, and show that only an oracle ML-based PLF benchmark can asymptotically attain the SLF CRB under restrictive conditions. Based on these results, we formulate a joint RE-selection and power-allocation problem under network-wide RE exclusivity, per-BS power budgets, a communication sum-rate constraint, and a sidelobe-amplitude constraint on the delay-Doppler ambiguity function. An efficient solution is developed via Schur-complement reformulations and penalty-based alternating optimization. Numerical results validate the analysis, demonstrate effective ambiguity-sidelobe suppression and consistent localization/velocity gains over representative baselines, while revealing geometry-dependent SLF-PLF performance gaps.

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