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

TL;DR

This paper introduces a digital twin framework for millimeter wave MIMO systems that efficiently learns site-specific beam codebooks, improving coverage and data rates while reducing interaction overhead.

Contribution

It proposes a novel digital twin aided learning method for site-specific beam codebooks, incorporating environment geometry and user types, with analysis of digital twin fidelity effects.

Findings

Digital twin-based codebooks adapt well to environment geometry.

Learning from digital twins enhances received signal-to-noise ratio.

Ray-tracing accuracy critically affects codebook performance.

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.