通过条件GAN实现蜂窝网络中的近完美覆盖流形估计
Near-perfect Coverage Manifold Estimation in Cellular Networks via conditional GAN
AI总结：
提出一种cGAN模型，将基站位置信息转换为区域覆盖概率，相比现有CNN预测误差降低两个数量级，且生成的覆盖流形视觉上近乎完美。
摘要：
本文提出了一种条件生成对抗网络（cGAN），可将任意感兴趣区域（RoI）的基站位置（BSL）信息转换为该区域子集（即评估区域，RoE）内依赖于位置的覆盖概率值。我们利用印度、美国、德国和巴西的BSL数据训练网络。与最先进的卷积神经网络（CNN）相比，该模型将预测误差（网络生成的覆盖流形与仿真生成的覆盖流形之间的L1差异）降低了两个数量级。此外，cGAN生成的覆盖流形在视觉上几乎与真实值无法区分。
英文摘要：
This paper 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
