Leveraging Channel Knowledge Map for Multi-User Hierarchical Beam Training Under Position Uncertainty

2025-11-28

AI Summary

In beam training, the absence of user location priors hinders effective utilization of Channel Knowledge Maps (CKMs), while the coupling mechanisms among CKMs, real-time observations, and training strategies remain unclear. Method: This paper proposes a reward-driven hierarchical beam search framework that explicitly models location uncertainty, integrates partial location priors with CKMs, and introduces a novel reward-guided pruned binary search tree. It further incorporates a low-complexity two-level lookahead strategy and correlation-aware multi-user location pruning to exploit inter-user interference sidelobe gains. Contribution/Results: Simulation results demonstrate that the proposed method significantly reduces training overhead—by up to 58% compared to baselines—in both single- and multi-user scenarios, while improving beam alignment efficiency. The framework provides a scalable, low-overhead solution for practical CKM deployment in 6G wireless systems.

Technology Category

• Search and Optimization: Learning to Search
• Planning, Routing, and Scheduling: Planning/Scheduling and Learning
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Channel knowledge map (CKM) emerges as a promising framework to acquire location-specific channel information without consuming wireless resources, creating new horizons for advanced wireless network design and optimization. Despite its potential, the practical application of CKM in beam training faces several challenges. On one hand, the user's precise location is typically unavailable prior to beam training, which limits the utility of CKM since its effectiveness relies heavily on accurate input of position data. On the other hand, the intricate interplay among CKM, real-time observations, and training strategies has not been thoroughly studied, leading to suboptimal performance and difficulties in practical implementation. In this paper, we present a framework for CKM-aided beam training that addresses these limitations. For single-user scenario, we propose a reward-motivated beam-potential hierarchical strategy which integrates partial position information and CKM. This strategy models the user equipment (UE) position uncertainty and formulates the hierarchical searching process as a pruned binary search tree. An optimal hierarchical searching strategy with minimal overhead is derived by evaluating the weights and rewards of potential codewords. Furthermore, a low-complexity two-layer lookahead scheme is designed to balance overhead and computational demands. For multi-user scenario, we develop a correlation-driven position-pruning training scheme, where sidelobe gains from inter-user interference are exploited to provide additional side information for overhead reduction, allowing all users to be simultaneously assigned their respective supportive beams. Simulations validate the superior performances of proposed approaches in advancing 6G beam training.

Problem

• Addresses beam training challenges when user positions are uncertain
• Integrates channel knowledge maps with real-time observations for optimization
• Develops hierarchical strategies for single-user and multi-user scenarios

Innovation

• Hierarchical beam search using pruned binary tree for single-user
• Two-layer lookahead scheme balances overhead and computational complexity
• Correlation-driven pruning exploits interference for multi-user beam training