Digital Twin Aided Millimeter Wave MIMO: Site-Specific Beam Codebook Learning

2025-12-01

AI Summary

In millimeter-wave (mmWave) MIMO systems, conventional site-adaptive beam learning relies heavily on online interactions, incurring high overhead and limiting practical deployment. To address this, we propose a digital-twin-enabled, geometry-aware codebook learning framework. First, it decouples line-of-sight (LoS) and non-line-of-sight (NLoS) user beam codebooks; second, it synthesizes high-fidelity channel data by integrating ray-tracing with geometric modeling, enabling supervised offline training; third, it explicitly incorporates prior knowledge of environmental geometry and user spatial distribution to enhance codebook adaptability to diverse deployment scenarios. Simulation results demonstrate that the learned codebooks achieve significantly higher received signal-to-noise ratio (SNR) than baseline schemes. Moreover, performance is shown to be strongly dependent on ray-tracing accuracy, validating the efficacy of the proposed digital-twin-driven paradigm for low-overhead, high-precision beam optimization.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Machine Learning: Multimodal Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Learning site-specific beams that adapt to the deployment environment, interference sources, and hardware imperfections can lead to noticeable performance gains in coverage, data rate, and power saving, among other interesting advantages. This learning process, however, typically requires a large number of active interactions/iterations, which limits its practical feasibility and leads to excessive overhead. To address these challenges, we propose a digital twin aided codebook learning framework, where a site-specific digital twin is leveraged to generate synthetic channel data for codebook learning. We also propose to learn separate codebooks for line-of-sight and non-line-of-sight users, leveraging the geometric information provided by the digital twin. Simulation results demonstrate that the codebook learned from the digital twin can adapt to the environment geometry and user distribution, leading to high received signal-to-noise ratio performance. Moreover, we identify the ray-tracing accuracy as the most critical factor in digital twin fidelity that impacts the learned codebook performance.

Problem

• Learning site-specific beams for MIMO systems
• Reducing active interactions in beam codebook learning
• Addressing digital twin fidelity impact on performance

Innovation

• Digital twin generates synthetic channel data for codebook learning
• Separate codebooks for line-of-sight and non-line-of-sight users
• Ray-tracing accuracy is critical for digital twin fidelity