Deep-Learning-Based Millimeter-Wave Massive MIMO for Hybrid Precoding
Deep-Learning-Based Millimeter-Wave Massive MIMO for Hybrid Precoding
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DOI：
10.1109/tvt.2019.2893928
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发表时间：
2019-03-01
影响因子：
6.8
通讯作者：
Adachi, Fumiyuki
中科院分区：
文献类型：
--
作者：
Huang, Hongji;Song, Yiwei;Adachi, Fumiyuki
毫米波（mmWave）大规模多输入多输出（MIMO）被认为是下一代通信的新兴解决方案，其中混合模拟和数字预编码是降低混合信号分量相关硬件复杂性和能耗的重要方法。然而，现有混合预编码方案的根本局限性在于计算复杂度高，不能充分利用空间信息。为了克服这些限制，本文提出了一种支持深度学习的毫米波大规模MIMO框架，用于有效的混合预编码，其中用于获得优化解码器的每个预编码器的选择被视为深度神经网络（DNN）中的映射关系。具体而言，通过基于深度神经网络的训练选择混合预编码器，优化毫米波大规模MIMO的预编码过程。此外，我们还提供了大量的仿真结果来验证该方案的优异性能。结果表明，基于dnn的方法能够最小化误码率并提高毫米波大规模MIMO的频谱效率，与传统方案相比，混合预编码的性能更好，同时大大降低了所需的计算复杂度。
Millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) has been regarded to be an emerging solution for the next generation of communications, in which hybrid analog and digital precoding is an important method for reducing the hardware complexity and energy consumption associated with mixed signal components. However, the fundamental limitations of the existing hybrid precoding schemes are that they have high-computational complexity and fail to fully exploit the spatial information. To overcome these limitations, this paper proposes a deep-learning-enabled mmWave massive MIMO framework for effective hybrid precoding, in which each selection of the precoders for obtaining the optimized decoder is regarded as a mapping relation in the deep neural network (DNN). Specifically, the hybrid precoder is selected through training based on the DNN for optimizing precoding process of the mmWave massive MIMO. Additionally, we present extensive simulation results to validate the excellent performance of the proposed scheme. The results exhibit that the DNN-based approach is capable of minimizing the bit error ratio and enhancing the spectrum efficiency of the mmWave massive MIMO, which achieves better performance in hybrid precoding compared with conventional schemes while substantially reducing the required computational complexity.