The downlink channel estimation requires a huge pilot overhead in the reconfigurable intelligent surface (RIS) assisted communication system. By exploiting the powerful learning ability of the neural network, the machine learning (ML) technique can be used to estimate the high-dimensional channel from a few received pilot signals at the user. However, since the training dataset collected by the single user only contains the information of part of the channel scenarios of a cell, the neural network trained by the single user is not able to work when the user moves from one channel scenario to another. To solve this challenge, we propose to leverage the distributed machine learning (DML) technique to enable the reliable downlink channel estimation. Specifically, we firstly build a downlink channel estimation neural network shared by all users, which can be collaboratively trained by the BS and the users with the help of the DML technique. Then, we further propose a hierarchical neural network architecture to improve the channel estimation accuracy, which can extract different channel features for different channel scenarios. Simulation results show that compared with the neural network trained by the single user, the proposed DML based neural networks can achieve better estimation performance with the reduced pilot overhead for all users from different scenarios.

中文翻译：

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基于分布式机器学习的 RIS 辅助无线通信下行链路信道估计

在可重构智能地面（RIS）辅助通信系统中，下行信道估计需要巨大的导频开销。通过利用神经网络强大的学习能力，机器学习（ML）技术可用于根据用户接收到的少量导频信号来估计高维信道。但是，由于单用户采集的训练数据集只包含一个小区的部分通道场景的信息，当用户从一个通道场景移动到另一个通道场景时，单用户训练的神经网络无法工作。为了解决这一挑战，我们建议利用分布式机器学习 (DML) 技术来实现可靠的下行链路信道估计。具体来说，我们首先构建了一个所有用户共享的下行信道估计神经网络，BS和用户可以在DML技术的帮助下协同训练。然后，我们进一步提出了一种分层神经网络架构来提高信道估计精度，可以针对不同的信道场景提取不同的信道特征。仿真结果表明，与单用户训练的神经网络相比，所提出的基于 DML 的神经网络在不同场景下所有用户的导频开销降低的情况下，可以实现更好的估计性能。