Millimeter Wave Channel Modeling via Generative Neural Networks
Millimeter Wave Channel Modeling via Generative Neural Networks
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DOI：
10.1109/gcwkshps50303.2020.9367420
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发表时间：
2020-08
期刊：
影响因子：
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通讯作者：
William Xia;S. Rangan;M. Mezzavilla;A. Lozano;Giovanni Geraci;V. Semkin;Giuseppe Loianno
中科院分区：
文献类型：
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作者：
William Xia;S. Rangan;M. Mezzavilla;A. Lozano;Giovanni Geraci;V. Semkin;Giuseppe Loianno
统计渠道模型有助于设计和评估无线通信系统。在毫米波带中，这样的模型变得极具挑战性。他们必须捕获每个链接和高分辨率的延迟，方向和路径增益。本文提出了一种基于数据的培训生成神经网络的一般建模方法。所提出的生成模型由一个两阶段结构组成，该结构首先预测每个链接的状态（视线，非视线或中断），随后将该状态馈入有条件的变异自动编码器，从而产生路径损失，延迟以及到达和出发的所有传播路径。重要的是，提出了最少的先验假设，从而使模型能够在数据中捕获复杂的关系。在城市环境中为28GHz空对地渠道展示了该方法，并通过射线追踪产生了训练数据集。
Statistical channel models are instrumental to design and evaluate wireless communication systems. In the millimeter wave bands, such models become acutely challenging; they must capture the delay, directions, and path gains, for each link and with high resolution. This paper presents a general modeling methodology based on training generative neural networks from data. The proposed generative model consists of a two-stage structure that first predicts the state of each link (line-of-sight, non-line-of-sight, or outage), and subsequently feeds this state into a conditional variational autoencoder that generates the path losses, delays, and angles of arrival and departure for all its propagation paths. Importantly, minimal prior assumptions are made, enabling the model to capture complex relationships within the data. The methodology is demonstrated for 28GHz air-to-ground channels in an urban environment, with training datasets produced by means of ray tracing.