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arXiv 2009.01423eess.SP
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基于深度残差学习的智能反射面辅助多用户通信信道估计

Deep Residual Learning for Channel Estimation in Intelligent Reflecting Surface-Assisted Multi-User Communications

Chang Liu, Xuemeng Liu, Derrick Wing Kwan Ng, Jinhong Yuan

摘要:

信道估计是实现实用的智能反射面辅助多用户通信系统的主要任务之一。然而,与传统的通信系统不同,IRS-MC系统通常涉及具有复杂统计分布的级联信道。在这种情况下,最优最小均方误差(MMSE)估计器需要计算多维积分,这在实际中是难以实现的。为了进一步提高信道估计的性能,在本文中,我们将信道估计建模为去噪问题,并采用深度残差学习(DReL)方法来隐式学习残差噪声,以便从基于噪声导频的观测中恢复信道系数。为此,我们首先开发了一个通用的基于DReL的信道估计框架,其中基于贝叶斯哲学的深度残差网络(DRN)的MMSE估计器。作为所开发的DReL框架的实现,基于卷积神经网络(CNN)的DRN(CDRN)被提出用于IRS-MC系统中的信道估计,其中配备有逐元素减法结构的CNN去噪块被专门设计为同时利用噪声信道矩阵的空间特征和噪声的加性。特别是,一个明确的表达建议CDRN推导和分析的贝叶斯估计,从理论上表征其属性。最后,仿真结果表明,所提出的方法的性能接近的最优MMSE估计要求的先验概率密度函数的可用性的信道。

英文摘要:

Channel estimation is one of the main tasks in realizing practical intelligent reflecting surface-assisted multi-user communication (IRS-MC) systems. However, different from traditional communication systems, an IRS-MC system generally involves a cascaded channel with a sophisticated statistical distribution. In this case, the optimal minimum mean square error (MMSE) estimator requires the calculation of a multidimensional integration which is intractable to be implemented in practice. To further improve the channel estimation performance, in this paper, we model the channel estimation as a denoising problem and adopt a deep residual learning (DReL) approach to implicitly learn the residual noise for recovering the channel coefficients from the noisy pilot-based observations. To this end, we first develop a versatile DReL-based channel estimation framework where a deep residual network (DRN)-based MMSE estimator is derived in terms of Bayesian philosophy. As a realization of the developed DReL framework, a convolutional neural network (CNN)-based DRN (CDRN) is then proposed for channel estimation in IRS-MC systems, in which a CNN denoising block equipped with an element-wise subtraction structure is specifically designed to exploit both the spatial features of the noisy channel matrices and the additive nature of the noise simultaneously. In particular, an explicit expression of the proposed CDRN is derived and analyzed in terms of Bayesian estimation to characterize its properties theoretically. Finally, simulation results demonstrate that the performance of the proposed method approaches that of the optimal MMSE estimator requiring the availability of the prior probability density function of channel.

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