Towards Addressing the Spatial Sparsity of MDT Reports to Enable Zero Touch Network Automation

Abstract

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.