Millimeter wave (mmWave) cell-free MIMO achieves an extremely high rate while its beam alignment (BA) suffers from excessive overhead due to a large number of transceivers. Recently, user location and probing measurements are utilized for BA based on machine learning (ML) models, e.g., deep neural network (DNN). However, most of these ML models are centralized with high communication and computational overhead and give no specific consideration to practical issues, e.g., limited training data and real-time model updates. In this paper, we study the {probing} beam-based BA for mmWave cell-free MIMO downlink with the help of broad learning (BL). For channels without and with uplink-downlink reciprocity, we propose the user-side and base station (BS)-side BL-aided incremental collaborative BA approaches. Via transforming the centralized BL into a distributed learning with data and feature splitting respectively, the user-side and BS-side schemes realize implicit sharing of multiple user data and multiple BS features. Simulations confirm that the user-side scheme is applicable to fast time-varying and/or non-stationary channels, while the BS-side scheme is suitable for systems with low-bandwidth fronthaul links and a central unit with limited computing power. The advantages of proposed schemes are also demonstrated compared to traditional and DNN-aided BA schemes.

翻译：毫米波无蜂窝MIMO在实现极高数据速率的同时，其波束对齐因大量收发器存在而面临过度开销问题。近年来，基于机器学习模型（如深度神经网络DNN）的用户位置与探测测量被用于波束对齐。然而，这些机器学习模型大多采用集中式架构，存在较高的通信与计算开销，且未充分考虑实际应用中的限制因素（如有限训练数据和实时模型更新）。本文研究基于探测波束的毫米波无蜂窝MIMO下行链路波束对齐方法，并借助宽度学习BL实现。针对无上下行互易性及具有互易性的信道，分别提出用户侧与基站侧BL辅助的增量协作波束对齐方案。通过将集中式BL分别转化为数据分割与特征分割的分布式学习，用户侧与基站侧方案实现了多用户数据与多基站特征的隐式共享。仿真验证表明：用户侧方案适用于快时变和/或非平稳信道，而基站侧方案适用于低带宽前传链路及计算能力受限的中央单元系统。与传统及DNN辅助波束对齐方案相比，所提方案在性能上展现出显著优势。