基于深度展开的联合信道估计和混合波束形成

Kai Kang, Qiyu Hu, Yunlong Cai, Guanding Yu, J. Hoydis, Y. Eldar
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引用次数: 0

摘要

在这项工作中,我们提出了一种基于端到端深度展开神经网络(NN)的联合信道估计和混合波束形成(JCEHB)算法,以最大化大规模多输入多输出(MIMO)系统中的和速率。具体地说,分别对信道估计和混合波束形成提出了递推最小二乘(RLS)和随机连续凸逼近(SSCA)算法。我们考虑了一种混合时标方案,其中模拟波束形成矩阵是基于每帧一次的信道状态信息(CSI)统计设计的,而数字波束形成矩阵是基于等效的CSI矩阵在每个时隙设计的。仿真结果表明,该算法明显优于传统算法。
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Joint Channel Estimation and Hybrid Beamforming via Deep-Unfolding
In this work, we propose an end-to-end deep-unfolding neural network (NN) based joint channel estimation and hybrid beamforming (JCEHB) algorithm to maximize the sum rate in massive multiple-input multiple-output (MIMO) systems. Specifically, the recursive least-squares (RLS) and stochastic successive convex approximation (SSCA) algorithms are unfolded for channel estimation and hybrid beamforming, respectively. We consider a mixed-timescale scheme, where analog beamforming matrices are designed based on the channel state information (CSI) statistics once in each frame, while the digital beamforming matrices are designed at each time slot based on the equivalent CSI matrices. Simulation results show that the proposed algorithm can significantly outperform conventional algorithms.
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