In this paper, we propose a novel integrated sensing and communication (ISAC)-enabled dual-scale channel estimation framework, where large-scale channel estimation benefits from sensing, and the temporal variation of small-scale channel state information is modeled via channel aging. By characterizing the impact of angular sensing error on the communication spatial correlation matrix, we derive a closed form expression for the achievable rate under dual-scale channel estimation errors. Considering the different characteristics in time scales, we design the sensing duration for slow-varying large-scale channel and determine the update timing and frequency for fast-varying small-scale channel information within a given frame structure. We formulate an average achievable rate maximization problem under limited time resources and sensing Cramer-Rao bound (CRB) constraints, and propose a segmented golden based joint optimization algorithm to efficiently solve this nonconvex problem. Simulation results demonstrate that our proposed scheme achieves significant performance improvement compared with the benchmark schemes, which further validate that the system can leverage additional sensing capabilities to enhance communication efficiency.

翻译：本文提出了一种新颖的集成感知与通信（ISAC）支持的双尺度信道估计框架，其中大尺度信道估计受益于感知功能，而小尺度信道状态信息的时间变化则通过信道老化模型进行刻画。通过分析角度感知误差对通信空间相关矩阵的影响，我们推导了双尺度信道估计误差下可达速率的闭式表达式。考虑到不同时间尺度的特性差异，我们在给定帧结构内设计了针对慢变化大尺度信道的感知时长，并确定了快变化小尺度信道信息的更新时机与频率。我们在有限时间资源与感知克拉美-罗界（CRB）约束下，构建了平均可达速率最大化问题，并提出了一种基于分段黄金分割的联合优化算法来高效求解这一非凸问题。仿真结果表明，与基准方案相比，我们提出的方案实现了显著的性能提升，进一步验证了系统能够利用额外的感知能力来增强通信效率。