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

2025-07-12

AI Summary

Millimeter-wave (mmWave) MIMO initial access suffers from low beam alignment efficiency; existing deep learning approaches face bottlenecks including high data overhead, poor hardware adaptability, limited interpretability, and weak adversarial robustness. Method: This paper proposes a digital twin–driven, interpretable, and robust beam alignment framework. It integrates digital twin–based channel modeling with transfer learning to reduce reliance on real-world measurements; introduces deep SHAP for spatial-directional importance analysis—enabling significant dimensionality reduction in training; and incorporates Differentiable k-Nearest Neighbors (DkNN) for real-time anomaly detection of input beams. Contribution/Results: Experiments demonstrate a 70% reduction in required real-data samples, a 62% decrease in beam training overhead, an 8.5× improvement in anomaly detection robustness, and spectral efficiency approaching the theoretical optimum. Crucially, the framework ensures high transparency and decision trustworthiness through end-to-end interpretability.

Technology Category

• Machine Learning: Mixture of Experts (MoE)
• Search and Optimization: Sampling/Simulation-based Search
• Multiagent Systems: Multiagent Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

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.

Problem

• Enhance beam prediction robustness in mmWave MIMO systems
• Reduce data collection and beam training overhead
• Improve explainability and outlier detection in AI models

Innovation

• Digital Twin generates synthetic channel data
• Transfer learning refines model with minimal data
• SHAP and DkNN ensure transparency and robustness