论文标题
基于深度学习的覆盖范围和速率歧管估计
Deep Learning based Coverage and Rate Manifold Estimation in Cellular Networks
论文作者
论文摘要
本文提出了基于卷积神经网络的自动编码器（CNN-AE），以预测网络拓扑的位置依赖性率和覆盖率。我们使用印度，巴西，德国和美国的BS位置数据进行训练，并将其性能与基于随机几何（SG）的分析模型进行比较。与最合适的SG模型相比，CNN-AE将覆盖率和利率预测错误的利润分别提高到$ 40 \％$ $和$ 25 \％$。作为应用程序，我们提出了低复杂性，可证明是收敛的算法，使用经过训练的CNN-AE可以计算新的BS的位置，这些位置需要在网络中部署，以满足预定的空间异质性能目标。
This article proposes Convolutional Neural Network-based Auto Encoder (CNN-AE) to predict location-dependent rate and coverage probability of a network from its topology. We train the CNN utilising BS location data of India, Brazil, Germany, and the USA and compare its performance with stochastic geometry (SG) based analytical models. In comparison to the best-fitted SG-based model, CNN-AE improves the coverage and rate prediction errors by a margin of as large as $40\%$ and $25\%$ respectively. As an application, we propose a low complexity, provably convergent algorithm that, using trained CNN-AE, can compute locations of new BSs that need to be deployed in a network in order to satisfy pre-defined spatially heterogeneous performance goals.
