基于字典学习的毫米波MIMO系统混合波束形成

Li Zhu, Jiang Zhu, Shilian Wang, Li Hu, Qian Cheng
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引用次数: 1

摘要

发射端和接收端的混合波束形成技术可以显著提高毫米波(mmWave)多输入多输出(MIMO)通信系统的频谱效率,降低通信系统的复杂度。然而,当传输数据流数量较大时,现有的混合波束形成设计方案,如正交匹配追踪稀疏逼近算法(OMP),存在性能下降的问题。在本文中,我们提出了字典学习(DL)算法来进行毫米波MIMO系统的混合波束形成设计。仿真结果表明,基于DL的混合波束形成的频谱效率接近无约束的最优波束形成,远优于基于OMP的混合波束形成的频谱效率。此外,还验证了该算法的收敛性。
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Hybrid Beamforming Based on Dictionary Learning for Millimeter Wave MIMO System
Hybrid beamforming in transmitter and receiver can improve the spectral efficiency of millimeterwave (mmWave) multiple-input multiple-output (MIMO) communication system significantly, and can reduce the complexity of communication system. However, when the number of transmitted data streams is large, the existing designing schemes of hybrid beamforming, such as sparse approximation by orthogonal matching pursuit(OMP) algorithm, suffer from performance degradation. In this paper, we propose dictionary learning (DL) algorithm to perform the design of hybrid beamforming for mmWave MIMO system. Simulation result shows that the spectral efficiency of hybrid beamforming based on DL approaches that of optimal beamforming without constraint, which is far superior to the spectral efficiency of hybrid beamforming based on OMP. Moreover, the convergence property of the proposed algorithm is also verified.
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