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Knowledge-Driven Machine Learning-based Channel Estimation in Massive MIMO System

机译：大型MIMO系统中基于知识驱动的机器学习的信道估计

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Accurate channel state information is critical for the massive multiple-input multiple-output(MIMO) system. However, due to the fundamental issue of pilot contamination, most existing works have to use additional channel information or complex machine learning network to design channel estimator. In this paper, we propose a knowledge-driven machine learning (KDML)-based channel estimator, which has a simple network structure and low training overhead. Firstly, we analyze the channel estimation problem under both uncorrelated and correlated channel models and convert it into the well-studied image denoising problem. Then, without any prior information about channel, we take advantage of the traditional channel estimation algorithms to construct the knowledge module in KDML, which maps the input data into object space without any training. After that, we use the denoising convolutional neural network (DnCNN) to build the learning module in KDML, where the residual learning accelerates the training process neural network. Finally, we obtain the estimation of the latent noise from the polluted channel matrix and then calculate the output of the proposed channel estimator. Simulation results demonstrate that the proposed channel estimator outperforms the traditional channel estimator in terms of the normalized mean-squared error (NMSE) without any statistical information about the channels. Besides, the proposed estimator also significantly better than the conventional machine learning-based estimator under the same network depth.
机译：精确的信道状态信息对于大规模多输入多输出（MIMO）系统至关重要。但是，由于导频污染的基本问题，大多数现有工程必须使用附加的渠道信息或复杂的机器学习网络来设计信道估计。在本文中，我们提出了一种知识驱动的机器学习（KDML）的信道估计器，其具有简单的网络结构和低训练开销。首先，我们在不相关和相关的信道模型下分析信道估计问题，并将其转换为熟练的图像去噪问题。然后，在没有关于频道的任何先前信息，我们利用传统的信道估计算法来构建KDML中的知识模块，将输入数据映射到对象空间而不进行任何培训。之后，我们使用去噪卷积神经网络（DNCNN）以KDML构建学习模块，其中剩余学习加速训练过程神经网络。最后，我们获得了来自污染信道矩阵的潜伏噪声的估计，然后计算所提出的信道估计器的输出。仿真结果表明，所提出的信道估计器在没有关于通道的任何统计信息的情况下，在归一化平均平方误差（NMSE）方面优于传统信道估计器。此外，所提出的估计师在相同的网络深度下也明显优于传统的基于机器学习的估计器。

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