Update Strategy for Channel Knowledge Map in Complex Environments

2025-12-17

AI Summary

To address the fundamental trade-off between channel knowledge map (CKM) aging-induced inaccuracy and update overhead in dynamic 6G channel environments, this paper proposes a utility-driven adaptive CKM update framework. First, a Map Efficacy Function is formulated to uniformly characterize CKM performance degradation under both gradual aging and abrupt interference. Second, a fractional programming model is established to maximize the long-term utility-overhead ratio. Third, we devise two novel algorithms: Delta-P, a globally optimal Dinkelbach-type algorithm, and Delta-L, a near-linear-complexity variant; both yield provably optimal threshold-based update policies. The framework enables theoretically optimal update timing under unpredictable channel dynamics and supports long-term utility maximization when channel evolution is predictable. Experiments demonstrate that, compared to baseline schemes, the proposed method reduces redundant updates by 42% and improves average CKM accuracy by 27%, effectively balancing modeling fidelity and resource efficiency.

Technology Category

• Planning, Routing, and Scheduling: Model-Based Reasoning
• Machine Learning: Calibration & Uncertainty Quantification
• Search and Optimization: Learning to Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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a reliable CKM demands substantial data and computation, and in dynamic environments, a pre-built CKM becomes outdated, degrading performance.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 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.

Problem

• Balancing CKM accuracy and update overhead in dynamic environments.
• Developing efficient algorithms for optimal update scheduling decisions.
• Adapting update strategies based on environmental predictability and degradation.

Innovation

• Map Efficacy Function models CKM aging and transitions
• Dinkelbach-based algorithms optimize update scheduling efficiently
• Threshold policy adapts to predictable and unpredictable environments