Bi-static sensing is crucial for exploring the potential of networked sensing capabilities in integrated sensing and communications (ISAC). However, it suffers from the challenging clock asynchronism issue. CSI ratio-based sensing is an effective means to address the issue. Its performance bounds, particular for Doppler sensing, have not been fully understood yet. This work endeavors to fill the research gap. Focusing on a single dynamic path in high-SNR scenarios, we derive the closed-form CRB. Then, through analyzing the mutual interference between dynamic and static paths, we simplify the CRB results by deriving close approximations, further unveiling new insights of the impact of numerous physical parameters on Doppler sensing. Moreover, utilizing the new CRB and analyses, we propose novel waveform optimization strategies for noise- and interference-limited sensing scenarios, which are also empowered by closed-form and efficient solutions. Extensive simulation results are provided to validate the preciseness of the derived CRB results and analyses, with the aid of the maximum-likelihood estimator. The results also demonstrate the substantial enhanced Doppler sensing accuracy and the sensing capabilities for low-speed target achieved by the proposed waveform design.

翻译：双基地感知对于挖掘集成感知与通信（ISAC）中网络化感知能力的潜力至关重要，然而其面临棘手的时钟异步问题。基于信道状态信息（CSI）比值的感知是解决该问题的有效手段，但其性能界，尤其是针对多普勒感知的性能界尚未被充分理解。本文致力于填补这一研究空白。针对高信噪比（SNR）场景下的单一动态路径，我们推导了闭合形式的克拉美-罗界（CRB）。进而，通过分析动态路径与静态路径间的互干扰，我们利用推导的紧密近似简化了CRB结果，进一步揭示了众多物理参数对多普勒感知的影响新见解。此外，基于新的CRB与分析结果，我们提出了面向噪声受限与干扰受限感知场景的新型波形优化策略，并赋予其闭合形式的有效求解方案。借助最大似然估计器，大量仿真结果验证了所推CRB结果与分析的精确性。结果还表明，所提波形设计显著提升了多普勒感知精度，并增强了对低速目标的感知能力。