Millimeter wave (mmWave) and terahertz MIMO systems rely on pre-defined beamforming codebooks for both initial access and data transmission. However, most of the existing codebooks adopt pre-defined beams that focus mainly on improving the gain of their target users, without taking interference into account, which could incur critical performance degradation in dense networks. To address this problem, in this paper, we propose a sample-efficient digital twin-assisted beam pattern design framework that learns how to form the beam pattern to reject the signals from the interfering directions. The proposed approach does not require any explicit channel knowledge or any coordination with the interferers. The adoption of the digital twin improves the sample efficiency by better leveraging the underlying signal relationship and by incorporating a demand-based data acquisition strategy. Simulation results show that the developed signal model-based learning framework can significantly reduce the actual interaction with the radio environment (i.e., the number of measurements) compared to the model-unaware design, leading to a more practical and efficient interference-aware beam design approach.

翻译：毫米波（mmWave）与太赫兹MIMO系统依赖预定义的波束赋形码本进行初始接入和数据传输。然而，现有大多数码本采用仅致力于提升目标用户增益的预定义波束，未考虑干扰因素，这在密集网络中可能导致严重的性能退化。针对该问题，本文提出一种样本高效的数字孪生辅助波束图案设计框架，通过学习如何形成波束图案以抑制来自干扰方向的信号。所提方法无需任何显式信道知识或与干扰源的协同配合。通过更优地利用底层信号关系并结合需求驱动的数据采集策略，数字孪生的引入提升了样本效率。仿真结果表明，相较于非模型感知设计，所开发的基于信号模型的学习框架能显著减少与无线环境的实际交互（即测量次数），形成更实用且高效的干扰感知波束设计方案。