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Deep Learning for mmWave Beam and Blockage Prediction Using Sub-6GHz Channels
IEEE Transactions on Communications ( IF 8.3 ) Pub Date : 2020-09-01 , DOI: 10.1109/tcomm.2020.3003670
Muhammad Alrabeiah , Ahmed Alkhateeb

Predicting the millimeter wave (mmWave) beams and blockages using sub-6 GHz channels has the potential of enabling mobility and reliability in scalable mmWave systems. Prior work has focused on extracting spatial channel characteristics at the sub-6 GHz band and then use them to reduce the mmWave beam training overhead. This approach still requires beam refinement at mmWave and does not normally account for the different dielectric properties at the different bands. In this paper, we first prove that under certain conditions, there exist mapping functions that can predict the optimal mmWave beam and blockage status directly from the sub-6 GHz channel. These mapping functions, however, are hard to characterize analytically which motivates exploiting deep neural network models to learn them. For that, we prove that a large enough neural network can predict mmWave beams and blockages with success probabilities that can be made arbitrarily close to one. Then, we develop a deep learning model and empirically evaluate its beam/blockage prediction performance using a publicly available dataset. The results show that the proposed solution can predict the mmWave blockages with more than 90% success probability and can predict the optimal mmWave beams to approach the upper bounds while requiring no beam training overhead.

中文翻译:

使用低于 6GHz 信道进行毫米波波束和阻塞预测的深度学习

使用低于 6 GHz 的信道预测毫米波 (mmWave) 波束和阻塞有可能在可扩展的毫米波系统中实现移动性和可靠性。之前的工作集中在提取 6 GHz 以下频段的空间信道特征,然后使用它们来减少毫米波波束训练开销。这种方法仍然需要在毫米波处进行波束细化,并且通常不考虑不同频段的不同介电特性。在本文中,我们首先证明在某些条件下,存在可以直接从 sub-6 GHz 信道预测最佳毫米波波束和阻塞状态的映射函数。然而,这些映射函数很难分析表征,这促使利用深度神经网络模型来学习它们。为了那个原因,我们证明了一个足够大的神经网络可以预测毫米波波束和阻塞,成功概率可以任意接近于 1。然后,我们开发了一个深度学习模型,并使用公开可用的数据集对其波束/阻塞预测性能进行经验评估。结果表明,所提出的解决方案可以以超过 90% 的成功概率预测毫米波阻塞,并且可以预测最佳毫米波波束以接近上限,同时不需要波束训练开销。
更新日期:2020-09-01
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