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.

翻译：本文提出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）信息转换为该区域内子集（即评估区域（RoE））中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数据对网络进行训练。与最先进的卷积神经网络（CNN）相比，我们的模型将预测误差（所考虑网络生成的覆盖流形与仿真生成的覆盖流形之间的$L_1$差异）提升了两个数量级。此外，cGAN生成的覆盖流形在视觉上几乎与真实结果无法区分。