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

TL;DR

This work introduces a digital twin aided framework for learning site-specific mmWave MIMO beam codebooks, aiming to reduce data collection overhead while achieving high SNR by leveraging synthetic channels generated from a site-specific digital twin. It decouples codebook design into separate LoS/NLoS schemes and employs deep reinforcement learning (DDPG) within the twin to learn quantized beamforming vectors, guided by clustering of synthetic channels. Simulations in a Times Square-like urban environment show the learned codebooks outperform traditional DFT codebooks, with ray-tracing fidelity identified as the critical factor influencing performance. The approach demonstrates the practicality and effectiveness of digital twins for geometry-aware, low-overhead beam codebook design in mmWave deployments.

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.

Figures (6)

• Figure 1: This diagram illustrates the proposed digital twin aided codebook learning framework. A digital replica of the real-world environment is constructed to generate synthetic channel information for codebook learning. The learned codebook can then be applied to real-world deployments.
• Figure 2: The figure shows the adopted target scenario, which is built based on Times Square in Manhattan. The base station is positioned at a building, and the user grid is highlighted by the red box.
• Figure 3: This figure shows the CDF of the received SNR in the target scenario for using separate codebooks for LoS and NLoS users compared to using a single codebook. The results indicate that employing separate codebooks enhances performance in the regions that require more beam angles to cover.
• Figure 4: This figure shows the CDF of the received SNR for the target scenario, the digital twin (DT) scenario, and the DFT beams. The results show that the codebook learned from the digital twin can achieve better performance than the DFT codebook in both LoS and NLoS regions.
• Figure 5: This figure presents the sensitivity analysis of the proposed approach with respect to the 3D geometry, EM material, and ray tracing. The results show that the ray tracing accuracy has a significant impact on the performance, while the geometry and EM material have a relatively minor effect.