cs.ITOct 31, 2026

Digital Twin-Assisted Fast Beam Alignment for mmWave MIMO Networks

Authors: Muhammad Kamran Saeed, Ashfaq Khokhar

Organizations: Dept. of Electrical and Computer Engineering, Iowa State University, Ames, IA, USA · College of Engineering, Kansas State University, Manhattan, KS, USA

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Abstract

Millimeter-wave (mmWave) communication relies on directional beam sweeping for initial access, which incurs substantial latency and signaling overhead as antenna arrays grow large and deployments become denser. Machine-learning-based beam prediction can reduce this overhead, but its effectiveness depends on training data that accurately reflects the deployment environment, a requirement that is difficult to meet in practice. This paper presents a latency-aware beam management architecture that integrates digital-twin-assisted prediction, regionbased localization, weighted ensemble learning, and explainability-driven beam pruning into a unified framework. A digital twin (DT) generates site-specific ray-traced channel knowledge offline, enabling environment-aware beam prediction without exhaustive real-world measurements and supporting both initial access and beam tracking. The proposed ensemble learning scheme combines a base classifier and a specialist classifier through reliability-weighted fusion to improve prediction accuracy and robustness, while explainable artificial intelligence (XAI)-based attribution pruning reduces the beam codebook to a small subset of the most informative beams for beam allocation. To capture the distributional mismatch between the DT and real-world environments: the network was trained on imperfect digital-twin data but evaluated on real-world surrobase data. Results show that the proposed method achieves 96.80% top-1 and 98.36% top-3 beam prediction accuracy, while reducing over-the-air sounding overhead by nearly an order of magnitude relative to exhaustive sweeping, outperforming convolutional and deep neural network baselines from prior work.

Figures & tables

Fig. 1 : Downlink of a mmWave system with active users in the user grid.
Fig. 2 : User-to-region assignment under the overlapping region partition.
Fig. 3 : The proposed stages of the offline training and online inference framework
Fig. 4 : Performance comparison for Top-1, Top-2, and Top-3 beam prediction accuracy.
Fig. 5 : Top- kk beam prediction accuracy across different beam configurations.
Fig. 6 : Effect of the base confidence threshold tt on the proposed cascade. (a) Percentage of samples retained by the base (blue) and escalated to the specialist (orange). (b) Top- 11 beam-prediction accuracy of the proposed cascade against the standalone base (Base Model) and specialist (Specialist Model) baselines. (c) Computational complexity
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