Causal Beam Selection for Reliable Initial Access in AI-driven Beam Management

• URL: http://arxiv.org/abs/2508.16352v1
• Date: Fri, 22 Aug 2025 12:56:07 GMT
• Title: Causal Beam Selection for Reliable Initial Access in AI-driven Beam Management
• Authors: Nasir Khan, Asmaa Abdallah, Abdulkadir Celik, Ahmed M. Eltawil, Sinem Coleri,
• Abstract summary: Existing deep learning (DL)-based beam alignment methods often neglect the underlying causal relationships between inputs and outputs.<n>We propose a causally-aware DL framework that integrates causal discovery into beam management pipeline.
• Score: 27.860170227028963
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Efficient and reliable beam alignment is a critical requirement for mmWave multiple-input multiple-output (MIMO) systems, especially in 6G and beyond, where communication must be fast, adaptive, and resilient to real-world uncertainties. Existing deep learning (DL)-based beam alignment methods often neglect the underlying causal relationships between inputs and outputs, leading to limited interpretability, poor generalization, and unnecessary beam sweeping overhead. In this work, we propose a causally-aware DL framework that integrates causal discovery into beam management pipeline. Particularly, we propose a novel two-stage causal beam selection algorithm to identify a minimal set of relevant inputs for beam prediction. First, causal discovery learns a Bayesian graph capturing dependencies between received power inputs and the optimal beam. Then, this graph guides causal feature selection for the DL-based classifier. Simulation results reveal that the proposed causal beam selection matches the performance of conventional methods while drastically reducing input selection time by 94.4% and beam sweeping overhead by 59.4% by focusing only on causally relevant features.

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