Towards Addressing the Spatial Sparsity of MDT Reports to Enable Zero Touch Network Automation
Towards Addressing the Spatial Sparsity of MDT Reports to Enable Zero Touch Network Automation
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DOI：
10.1109/globecom46510.2021.9686011
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发表时间：
2021-12
期刊：
影响因子：
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通讯作者：
Joel Shodamola;H. Qureshi;Usama Masood;A. Imran
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文献类型：
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作者：
Joel Shodamola;H. Qureshi;Usama Masood;A. Imran
驾驶测试（MDT）报告的最小化是新兴蜂窝网络中基于机器学习（ML）的零接触自动化的关键推动因素。然而，由于诸多因素，MDT报告在空间上是稀疏的。这种稀疏性破坏了基于MDT数据构建的机器学习模型的性能，这些模型用于估计和优化网络kpi。在本文中，我们提出并评估了一个解决这一挑战的框架。我们利用生成模型，特别是生成对抗网络（GAN）和变分自编码器（VAE）来增强稀疏的多维MDT数据。与生成模型生成的合成图像的质量可以直观评估的图像数据不同，建立表格合成数据的真实性是一个更复杂的问题。我们通过利用三方面的方法来解决这个问题：1)我们使用几个统计度量来量化合成数据与原始数据的相似性。2)我们比较了在增强数据上训练的集成学习模型与仅在原始数据上训练的集成学习模型的性能。3)我们用几个经典的ML模型对生成模型的性能进行了基准测试。该分析针对不同级别的稀疏性进行，并揭示了关于生成模型对训练数据稀疏性的鲁棒性以及评估生成的合成表格数据质量的各种方法的适用性的见解。结果表明，与其他方法相比，GAN的性能要好得多。因此，所提出的解决方案可用于克服MDT报告中的稀疏性问题，从而支持基于ml的自动化用例。
Minimization of Drive Test (MDT) reports are a key enabler for Machine Learning (ML)-based zero-touch automation envisioned for emerging cellular networks. However, due to numerous factors, the MDT reports are spatially sparse in nature. This sparsity undermines the performance of ML models that are built on the MDT data to estimate and optimize network KPIs. In this paper, we present and evaluate a framework to address this challenge. We leverage generative models, specifically, Gener-ative Adversarial Networks (GAN) and Variational Autoencoders (VAE) to augment the sparse multi-dimensional MDT data. Unlike image data where the quality of synthetic images produced by the generative models can be evaluated visually, establishing the authenticity of tabular synthetic data is a more complex problem. We address this problem by leveraging a tripartite approach: 1) We use several statistical measures to quantify the resemblance of synthetic data with original data. 2) We compare the performance of an ensemble learning model trained on augmented data, with that of trained on original data only 3) We benchmark the performance of the generative models with several classical ML models. This analysis is carried out for varying levels of sparsity and reveals insights about robustness of generative models against training data sparsity as well as on suitability of various methods for evaluating the quality of the generated synthetic tabular data. Results show GAN performs considerably better compared to other approaches. The presented solution thus can be used to overcome the sparsity problem in MDT reports thereby enabling ML-based automation use cases.