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.

翻译：学习能够适应部署环境、干扰源及硬件缺陷的场景化波束，可在覆盖范围、数据速率和节能等方面带来显著性能提升，并具备其他潜在优势。然而，该学习过程通常需要大量主动交互/迭代，限制了其实际可行性并导致过高开销。为应对这些挑战，本文提出一种数字孪生辅助的码本学习框架，利用场景化数字孪生生成合成信道数据以进行码本学习。通过数字孪生提供的几何信息，我们还提出为视距与非视距用户分别学习专用码本。仿真结果表明，基于数字孪生学习的码本能够适应环境几何结构与用户分布，从而实现高接收信噪比性能。此外，我们指出光线追踪精度是影响数字孪生保真度的最关键因素，直接决定了所学码本的性能表现。