Channel knowledge map (CKM) is a promising paradigm for environment-aware communications by establishing a deterministic mapping between physical locations and channel parameters. Existing CKM construction methods focus on quasi-static propagation environment. This paper develops a dynamic CKM construction method for multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. We establish a dynamic channel model that captures the coexistence of quasi-static and dynamic scatterers, as well as the impacts of antenna rotation and synchronization errors. Based on this model, we formulate the problem of dynamic CKM construction within a Bayesian inference framework and design a two-stage approximate Bayesian inference algorithm. In stage I, a high-performance algorithm is developed to jointly infer quasi-static channel parameters and calibrate synchronization errors from historical measurements. In stage II, by leveraging the quasi-static parameters as informative priors, a low-complexity algorithm is designed to estimate dynamic parameters from limited real-time measurements. Simulation results validate the superiority of the proposed method and demonstrate its effectiveness in enabling low-overhead, high-performance channel estimation in dynamic environments.

翻译：信道知识地图（CKM）通过建立物理位置与信道参数之间的确定性映射，为实现环境感知通信提供了一种前景广阔的范式。现有CKM构建方法主要针对准静态传播环境。本文针对多输入多输出正交频分复用（MIMO-OFDM）系统，提出了一种动态CKM构建方法。我们建立了一个动态信道模型，该模型同时捕捉准静态散射体与动态散射体的共存特性，以及天线旋转和同步误差的影响。基于此模型，我们将动态CKM构建问题置于贝叶斯推断框架下进行建模，并设计了一种两阶段近似贝叶斯推断算法。在第一阶段，我们开发了一种高性能算法，用于从历史测量数据中联合推断准静态信道参数并校准同步误差。在第二阶段，通过将准静态参数作为信息先验，我们设计了一种低复杂度算法，利用有限的实时测量数据估计动态参数。仿真结果验证了所提方法的优越性，并证明了其在动态环境中实现低开销、高性能信道估计的有效性。