arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2012.00241eess.SPcs.ITmath.IT
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IRS辅助多用户通信系统的深度残差网络增强信道估计

Deep Residual Network Empowered Channel Estimation for IRS-Assisted Multi-User Communication Systems

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式表面辅助多用户通信系统的关键。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,这阻碍了贝叶斯估计器的实现。为了进一步提高信道估计的性能,本文将信道估计建模为去噪问题,并采用数据驱动的方法实现信道估计。具体而言,我们提出了一种基于卷积神经网络(CNN)的深度残差网络(CDRN),以隐式地学习残差噪声,用于从基于噪声导频的观测中恢复信道系数。在所提出的CDRN中,CNN去噪模块配备了逐元素减法结构,以同时利用噪声信道矩阵的空间特征和噪声的加性,这进一步提高了估计精度。仿真结果表明,该方法可以达到几乎相同的估计精度的最佳最小均方误差(MMSE)估计器需要的信道分布的知识。

英文摘要:

Channel estimation is of great importance 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, which hinders the implementations of the Bayesian estimators. To further improve the channel estimation performance, in this paper, we model the channel estimation as a denoising problem and adopt a data-driven approach to realize the channel estimation. Specifically, we propose a convolutional neural network (CNN)-based deep residual network (CDRN) to implicitly learn the residual noise for recovering the channel coefficients from the noisy pilot-based observations. In the proposed CDRN, a CNN denoising block equipped with an element-wise subtraction structure is designed to exploit both the spatial features of the noisy channel matrices and the additive nature of the noise simultaneously, which further improves the estimation accuracy. Simulation results demonstrate that the proposed method can almost achieve the same estimation accuracy as that of the optimal minimum mean square error (MMSE) estimator requiring the knowledge of the channel distribution.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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