Unsupervised mmWave Beamforming via Autoencoders
Unsupervised mmWave Beamforming via Autoencoders
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DOI：
10.1109/icc40277.2020.9149222
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发表时间：
2020-06
期刊：
影响因子：
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通讯作者：
T. Peken;R. Tandon;T. Bose
中科院分区：
文献类型：
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作者：
T. Peken;R. Tandon;T. Bose
我们提供了基于自动编码器的无监督机器学习（ML）方案，用于毫米波（mmWave）中的无约束波束成形（BF）和混合BF。自动编码器是一种功能强大的无监督ML模型，它用于通过找到输入的低维表示来以最小的错误重建输入。在本文中，我们提出了一种线性自动编码器，用于在发射机（Tx）和接收机（Rx）处找到波束形成器，从而最大限度地提高毫米波信道上的速率。由于自编码器与奇异值分解（SVD）有着密切的关系，我们首先研究了基于SVD的无约束BF自编码器。在混合波束赋形中，波束赋形器是通过在射频（RF）域中使用有限精度移相器来设计的，沿着功率约束。因此，我们提出了一种基于自编码器的混合BF算法，它结合了这些约束。我们提出了我们的模拟结果为无约束BF以及混合BF，并比较他们的性能与国家的最先进的。通过使用随机和NYUSIM信道模型，我们实现了30 - 40%和60 - 70%的收益率与建议的自动编码器为基础的方法相比，监督混合BF与随机和NYUSIM信道模型，分别。
We provide unsupervised machine learning (ML) schemes based on autoencoders for unconstrained beamforming (BF) and hybrid BF in millimeter-waves (mmWaves). An autoencoder is a powerful unsupervised ML model, and it is used to reconstruct the input with a minimal error by finding a low-dimensional representation of the input. In this paper, we present a linear autoencoder for finding the beamformers at the transmitter (Tx) and receiver (Rx), which maximize the achieved rates over the mmWave channel. Since the autoencoder has a close relationship with the singular value decomposition (SVD), we first study autoencoders for unconstrained BF based on SVD. In hybrid BF, beamformers are designed by using finite-precision phase shifters in the radio frequency (RF) domain along with power constraints. Therefore, we propose a hybrid BF algorithm based on autoencoders, which incorporates these constraints. We present our simulation results for both unconstrained BF as well as hybrid BF, and compare their performance with state-of-the-art. By using the stochastic and NYUSIM channel models, we achieve 30 - 40% and 60 - 70% gains in rates with the proposed autoencoder based approach compared to the supervised hybrid BF with the stochastic and NYUSIM channel models, respectively.