Causal Beam Selection for Reliable Initial Access in AI-driven Beam Management
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866911116695699456 |
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| author | Khan, Nasir Abdallah, Asmaa Celik, Abdulkadir Eltawil, Ahmed M. Coleri, Sinem |
| author_facet | Khan, Nasir Abdallah, Asmaa Celik, Abdulkadir Eltawil, Ahmed M. Coleri, Sinem |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_16352 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Causal Beam Selection for Reliable Initial Access in AI-driven Beam Management Khan, Nasir Abdallah, Asmaa Celik, Abdulkadir Eltawil, Ahmed M. Coleri, Sinem Artificial Intelligence Signal Processing 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. |
| title | Causal Beam Selection for Reliable Initial Access in AI-driven Beam Management |
| topic | Artificial Intelligence Signal Processing |
| url | https://arxiv.org/abs/2508.16352 |