Digital Twin-Assisted Explainable AI for Robust Beam Prediction in mmWave MIMO Systems

• URL: http://arxiv.org/abs/2507.14180v1
• Date: Sat, 12 Jul 2025 09:56:20 GMT
• Title: Digital Twin-Assisted Explainable AI for Robust Beam Prediction in mmWave MIMO Systems
• Authors: Nasir Khan, Asmaa Abdallah, Abdulkadir Celik, Ahmed M. Eltawil, Sinem Coleri,
• Abstract summary: This paper proposes a robust and explainable deep learning (DL)-based beam alignment engine for mmWave systems.<n>The framework reduces real-world data needs by 70%, beam training overhead by 62%, and improves outlier detection by up to 8.5x.<n> Experimental results show that the proposed framework reduces real-world data needs by 70%, beam training overhead by 62%, and improves outlier detection by up to 8.5x.
• Score: 18.49800990388549
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In line with the AI-native 6G vision, explainability and robustness are crucial for building trust and ensuring reliable performance in millimeter-wave (mmWave) systems. Efficient beam alignment is essential for initial access, but deep learning (DL) solutions face challenges, including high data collection overhead, hardware constraints, lack of explainability, and susceptibility to adversarial attacks. This paper proposes a robust and explainable DL-based beam alignment engine (BAE) for mmWave multiple-input multiple output (MIMO) systems. The BAE uses received signal strength indicator (RSSI) measurements from wide beams to predict the best narrow beam, reducing the overhead of exhaustive beam sweeping. To overcome the challenge of real-world data collection, this work leverages a site-specific digital twin (DT) to generate synthetic channel data closely resembling real-world environments. A model refinement via transfer learning is proposed to fine-tune the pre-trained model residing in the DT with minimal real-world data, effectively bridging mismatches between the digital replica and real-world environments. To reduce beam training overhead and enhance transparency, the framework uses deep Shapley additive explanations (SHAP) to rank input features by importance, prioritizing key spatial directions and minimizing beam sweeping. It also incorporates the Deep k-nearest neighbors (DkNN) algorithm, providing a credibility metric for detecting out-of-distribution inputs and ensuring robust, transparent decision-making. Experimental results show that the proposed framework reduces real-world data needs by 70%, beam training overhead by 62%, and improves outlier detection robustness by up to 8.5x, achieving near-optimal spectral efficiency and transparent decision making compared to traditional softmax based DL models.

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