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

arXiv: https://arxiv.org/abs/2508.16352 · HTML: https://mlsanity.com/paper/2508.16352/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 17 references that are in the map.
- First posted on arXiv 22 Aug 2025.

## Builds on (most cited first, 7 of 7)

- [DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applications](https://mlsanity.com/paper/1902.06435/) (2019)
- [A Survey on Causal Discovery: Theory and Practice](https://mlsanity.com/paper/2305.10032/) (2023)
- [A Tutorial on Beam Management for 3GPP NR at mmWave Frequencies](https://mlsanity.com/paper/1804.01908/) (2018)
- [Causal Reasoning: Charting a Revolutionary Course for Next-Generation AI-Native Wireless Networks](https://mlsanity.com/paper/2309.13223/) (2023)
- [Deep Learning for Fast and Reliable Initial Access in AI-Driven 6G mmWave Networks](https://mlsanity.com/paper/2101.01847/) (2021)
- [Explainable AI-aided Feature Selection and Model Reduction for DRL-based V2X Resource Allocation](https://mlsanity.com/paper/2501.13552/) (2025)
- [Digital Twin-Assisted Explainable AI for Robust Beam Prediction in mmWave MIMO Systems](https://mlsanity.com/paper/2507.14180/) (2025)

## Abstract (arXiv)

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.