The Channel Knowledge Map (CKM) maps position information to channel state information, leveraging environmental knowledge to reduce signaling overhead in sixth-generation networks. However, constructing a reliable CKM demands substantial data and computation, and in dynamic environments, a pre-built CKM becomes outdated, degrading performance. Frequent retraining restores accuracy but incurs significant waste, creating a fundamental trade-off between CKM efficacy and update overhead. To address this, we introduce a Map Efficacy Function (MEF) capturing both gradual aging and abrupt environmental transitions, and formulate the update scheduling problem as fractional programming. We develop two Dinkelbach-based algorithms: Delta-P guarantees global optimality, while Delta-L achieves near-optimal performance with near-linear complexity. For unpredictable environments, we derive a threshold-based policy: immediate updates are optimal when the environmental degradation rate exceeds the resource consumption acceleration; otherwise, delay is preferable. For predictable environments, long-term strategies strategically relax these myopic rules to maximize global performance. Across this regime, the policy reveals that stronger entry loss and faster decay favor immediate updates, while weaker entry loss and slower decay favor delayed updates.

翻译：信道知识地图（CKM）将位置信息映射至信道状态信息，利用环境知识以降低第六代网络中的信令开销。然而，构建可靠的CKM需要大量数据与计算资源，且在动态环境中，预先构建的CKM会逐渐过时，导致性能下降。频繁的重训练虽可恢复精度，但会产生显著的资源浪费，从而在CKM效能与更新开销之间形成根本性权衡。为解决此问题，我们引入了地图效能函数（MEF），该函数同时捕捉CKM的渐进老化与环境的突变转换，并将更新调度问题形式化为分式规划。我们开发了两种基于Dinkelbach的算法：Delta-P保证全局最优性，而Delta-L以近似线性的复杂度实现接近最优的性能。针对不可预测环境，我们推导出一种基于阈值的策略：当环境退化速率超过资源消耗加速率时，立即更新是最优选择；反之，则延迟更新更为适宜。对于可预测环境，长期策略会策略性地放宽这些短视规则，以最大化全局性能。在此机制下，策略分析表明：较强的初始损耗与较快的衰减倾向于支持立即更新，而较弱的初始损耗与较慢的衰减则倾向于支持延迟更新。