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

TL;DR

The paper tackles beam training under position uncertainty by leveraging a beam codeword–oriented CKM (BeamCKM) to provide location-aware priors. It introduces a reward-motivated beam-potential hierarchical training framework and a low-complexity two-layer lookahead to balance overhead and computation in single-user scenarios, and extends the approach with a correlation-driven, multi-user position-pruning scheme that exploits inter-user sidelobes for joint training. The proposed methods demonstrate reduced training overhead and substantial spectral efficiency gains in ray-traced simulations, validating CKM as a practical enabler for 6G beam management. The work paves the way for robust environment-aware beam training and scalable multi-user coordination under localization uncertainty.

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.

Figures (12)

• Figure 1: Technical roadmap and logical relationships of CKM-aided hierarchical beam training: CKM uniquely empowers beam training with CSI-related prior insights, while the measured beam gains serve as real-time observation (state) that provide direct guidance for feasible directions. The balance between these insights and real-time observations mitigates CKM's inherent inaccuracy and enhance beam training performance.
• Figure 2: Illustration of the multi-user hierarchical beam training system model.
• Figure 3: An illustration of beamCKM.
• Figure 4: An toy example of pruned search tree for CKM-aided hierarchical beam training.
• Figure 5: Three categories for topological structure of two-layer lookahead tree.