Update Strategy for Channel Knowledge Map in Complex Environments

📝 Original Info

• Title: Update Strategy for Channel Knowledge Map in Complex Environments
• ArXiv ID: 2512.15154
• Date: 2025-12-17
• Authors: Ting Wang, Chiya Zhang, Chang Liu, Zhuoyuan Hao, Rubing Han, Weizheng Zhang, Chunlong He

📝 Abstract

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a reliable CKM demands substantial data and computation, and in dynamic environments, a pre-built CKM becomes outdated, degrading performance.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 nearlinear 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, longterm strategies strategically relax these myopic rules to maximize global performance. Across this regime, the policy rev...

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