# Digital Twin-Assisted Explainable AI for Robust Beam Prediction in mmWave MIMO Systems arXiv: https://arxiv.org/abs/2507.14180 ยท HTML: https://mlsanity.com/paper/2507.14180/ ## Key facts - Cited by 2 papers in the map (data through 14 Oct 2026). - Builds on 25 of its 47 references that are in the map. - First posted on arXiv 12 Jul 2025. ## Builds on (most cited first, 25 of 25) - [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014) - [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017) - [Learning Important Features Through Propagating Activation Differences](https://mlsanity.com/paper/1704.02685/) (2017) - [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019) - [Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning](https://mlsanity.com/paper/1803.04765/) (2018) - [DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applications](https://mlsanity.com/paper/1902.06435/) (2019) - [Practical and Optimal LSH for Angular Distance](https://mlsanity.com/paper/1509.02897/) (2015) - [A Tutorial on Beam Management for 3GPP NR at mmWave Frequencies](https://mlsanity.com/paper/1804.01908/) (2018) - [Real-Time Digital Twins: Vision and Research Directions for 6G and Beyond](https://mlsanity.com/paper/2301.11283/) (2023) - [A Survey of Beam Management for mmWave and THz Communications Towards 6G](https://mlsanity.com/paper/2308.02135/) (2023) - [Radar Aided 6G Beam Prediction: Deep Learning Algorithms and Real-World Demonstration](https://mlsanity.com/paper/2111.09676/) (2021) - [Position Aided Beam Prediction in the Real World: How Useful GPS Locations Actually Are?](https://mlsanity.com/paper/2205.09054/) (2022) - [Digital Twin Based Beam Prediction: Can we Train in the Digital World and Deploy in Reality?](https://mlsanity.com/paper/2301.07682/) (2023) - [Explainable AI in 6G O-RAN: A Tutorial and Survey on Architecture, Use Cases, Challenges, and Future Research](https://mlsanity.com/paper/2307.00319/) (2023) - [AI/ML for Beam Management in 5G-Advanced: A Standardization Perspective](https://mlsanity.com/paper/2309.10575/) (2023) - [Learning Site-Specific Probing Beams for Fast mmWave Beam Alignment](https://mlsanity.com/paper/2107.13121/) (2021) - [Explanation-Guided Deep Reinforcement Learning for Trustworthy 6G RAN Slicing](https://mlsanity.com/paper/2303.15000/) (2023) - [Deep Learning Assisted Calibrated Beam Training for Millimeter-Wave Communication Systems](https://mlsanity.com/paper/2101.05206/) (2021) - [Deep Learning-based Beam Tracking for Millimeter-wave Communications under Mobility](https://mlsanity.com/paper/2102.09785/) (2021) - [Hierarchical Codebook based Multiuser Beam Training for Millimeter Wave Massive MIMO](https://mlsanity.com/paper/2203.06438/) (2022) - [Deep Learning for Fast and Reliable Initial Access in AI-Driven 6G mmWave Networks](https://mlsanity.com/paper/2101.01847/) (2021) - [Beam Management in Ultra-dense mmWave Network via Federated Reinforcement Learning: An Intelligent and Secure Approach](https://mlsanity.com/paper/2210.01307/) (2022) - [Explainable AI-aided Feature Selection and Model Reduction for DRL-based V2X Resource Allocation](https://mlsanity.com/paper/2501.13552/) (2025) - [Explainable and Robust Millimeter Wave Beam Alignment for AI-Native 6G Networks](https://mlsanity.com/paper/2501.17883/) (2025) - [Digital Twin Aided Compressive Sensing: Enabling Site-Specific MIMO Hybrid Precoding](https://mlsanity.com/paper/2405.07115/) (2024) ## Built on by (most cited first, 2 of 2) - [Heterogeneity-agnostic AI/ML-assisted beam selection for multi-panel arrays](https://mlsanity.com/paper/2602.18678/) (2026) - [Causal Beam Selection for Reliable Initial Access in AI-driven Beam Management](https://mlsanity.com/paper/2508.16352/) (2025) ## Abstract (arXiv) 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.