论文标题
在MM波大量MIMO中进行混合边界成形的联合学习
Federated Learning for Hybrid Beamforming in mm-Wave Massive MIMO
论文作者
论文摘要
使用集中式机器学习（CML）技术对混合波束形成的机器学习进行了广泛的研究，该技术需要培训全球模型，并从用户收集的大型数据集中进行培训。但是，由于通信带宽和隐私问题，用户和基站（BS）之间整个数据集的传输在计算方面非常过高。在这项工作中，我们引入了一个基于联合学习的框架（FL），用于混合波束形成，其中仅通过从用户那里收集梯度来在BS进行模型培训。我们设计了一个卷积神经网络，其中输入是通道数据，在输出处产生模拟波束形式。通过数值模拟，FL被证明对通道数据中的缺陷和损坏更宽容，并且与CML相比，开销较少。
Machine learning for hybrid beamforming has been extensively studied by using centralized machine learning (CML) techniques, which require the training of a global model with a large dataset collected from the users. However, the transmission of the whole dataset between the users and the base station (BS) is computationally prohibitive due to limited communication bandwidth and privacy concerns. In this work, we introduce a federated learning (FL) based framework for hybrid beamforming, where the model training is performed at the BS by collecting only the gradients from the users. We design a convolutional neural network, in which the input is the channel data, yielding the analog beamformers at the output. Via numerical simulations, FL is demonstrated to be more tolerant to the imperfections and corruptions in the channel data as well as having less transmission overhead than CML.
