通过条件 GAN 在蜂窝网络中实现近乎完美的覆盖态势估计

Washim Uddin Mondal;Veni Goyal;Satish V. Ukkusuri;Goutam Das;Di Wang;Mohamed-Slim Alouini;Vaneet Aggarwal
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引用次数: 0

摘要

本文介绍了一种条件生成对抗网络(cGAN),它能将任何感兴趣区域(RoI)的基站位置(BSL)信息转化为该区域(称为评估区域(RoE))内与位置相关的覆盖概率值。我们利用印度、美国、德国和巴西的 BSL 数据来训练我们的网络。与最先进的卷积神经网络(CNN)相比,我们的模型将预测误差(网络生成的覆盖流形与模拟生成的覆盖流形之间的 $L_1$ 差值)提高了两个数量级。此外,cGAN 生成的覆盖流形在视觉上与地面实况几乎没有区别。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Near-Perfect Coverage Manifold Estimation in Cellular Networks via Conditional GAN
This letter presents a conditional generative adversarial network (cGAN) that translates base station location (BSL) information of any Region-of-Interest (RoI) to location-dependent coverage probability values within a subset of that region, called the region-of-evaluation (RoE). We train our network utilizing the BSL data of India, the USA, Germany, and Brazil. In comparison to the state-of-the-art convolutional neural networks (CNNs), our model improves the prediction error ( $L_{1}$ difference between the coverage manifold generated by the network under consideration and that generated via simulation) by two orders of magnitude. Moreover, the cGAN-generated coverage manifolds appear to be almost visually indistinguishable from the ground truth.
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