The 6G communication systems use mmWave and MIMO technologies to achieve wide bandwidth and high throughout, leading to indispensable need for beam alignment to overcome severe signal attenuation. Traditional sector-search-based beam alignment algorithms rely on sequential sampling to identify the best sector, resulting in a significant latency burden on 6G communication systems. Recently proposed adaptive beam alignment algorithms based on the active learning framework address the problem, aiming to identify the optimal sector with the fewest possible samples under an identical sector partition. Nevertheless, these algorithms either lack feasibility (Chiu, Ronquillo and Javidi, JSAC 2019) due to ideal assumptions or lack interpretability (Sohrabi, Chen and Yu, JSAC 2021) due to the use of end-to-end black-box neural networks. To avoid ideal assumptions and maintain interpretability, we address all above problems by proposing an adaptive beam alignment algorithm using the framework of noisy twenty questions estimation with a trained questioner. Specifically, we use two methods for training the questioner to eliminate reliance on ideal assumptions. The first method maps queries of twenty questions estimation to beamforming vectors via weighted summation of steering vectors, as an initial attempt to address the feasibility problem encountered in prior pioneering study by Chiu, Ronquillo and Javidi (JSAC 2019). The second method uses multi-layer fully connected neural networks to achieve improved performance while only employing them to train the questioner, which can effectively mitigate the interpretability issues in prior study by Sohrabi, Chen and Yu (JSAC 2021). Furthermore, we provide numerical simulations to illustrate the effectiveness of our proposed adaptive beam alignment algorithms and demonstrate that our algorithms outperform all benchmark algorithms.

翻译：6G通信系统采用毫米波与大规模MIMO技术以实现宽带宽与高吞吐量，这导致波束对准成为克服严重信号衰减不可或缺的需求。传统的基于扇区搜索的波束对准算法依赖顺序采样来识别最佳扇区，给6G通信系统带来了显著的时延负担。近期提出的基于主动学习框架的自适应波束对准算法致力于解决此问题，其目标是在相同扇区划分下以尽可能少的样本识别最优扇区。然而，这些算法或因理想假设而缺乏可行性（Chiu, Ronquillo and Javidi, JSAC 2019），或因使用端到端黑盒神经网络而缺乏可解释性（Sohrabi, Chen and Yu, JSAC 2021）。为避免理想假设并保持可解释性，我们通过提出一种基于训练提问者的含噪二十问估计框架的自适应波束对准算法，解决了上述所有问题。具体而言，我们采用两种方法训练提问者以消除对理想假设的依赖。第一种方法通过导向矢量的加权求和将二十问估计的查询映射至波束赋形矢量，这是对Chiu、Ronquillo与Javidi（JSAC 2019）在先驱性研究中遇到的可行性问题的初步尝试性解决。第二种方法使用多层全连接神经网络以获得更优性能，同时仅将其用于训练提问者，这能有效缓解Sohrabi、Chen与Yu（JSAC 2021）先前研究中存在的可解释性问题。此外，我们通过数值仿真说明了所提自适应波束对准算法的有效性，并证明我们的算法性能优于所有基准算法。