Empowering mobile network planning through deep learning: a path to democratization

Abstract

In the realm of cellular network internet data traffic assessment, the imperative task of forecasting and comprehending traffic patterns assumes pivotal significance for the effective management of network-designed Quality of Service (QoS) benchmarks. Conventional methodologies employed for predicting data traffic often suffer from inaccuracies. These traditional traffic forecasts, typically conducted at a higherlevel or within generously sized regional cluster contexts, tend to exhibit limitations in terms of accuracy. Furthermore, the absence of readily accessible eNodeB-level utilization data in conjunction with traffic forecasting exacerbates these challenges. This, in turn, may lead to compromised user experiences or unwarranted network expansion decisions based on outdated methodologies. This research embarks upon an ambitious journey encompassing an extensive dataset encompassing 6.2 million real network time series data points derived from Long-Term Evolution (LTE) networks. It also delves into associated parameters, including eNodeB-wise Physical Resource Block (PRB) utilization. The core objective revolves around the development of a traffic forecasting model that harnesses multivariate feature inputs and cutting-edge deep learning algorithms. Various advanced deep learning algorithms, including Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and Gated Recurrent Unit (GRU), have been separately tested for training purposes, with the most suitable model being chosen among the three for eNodeB-level predictions. This state-of-the-art deep learning model not only enables highly granular eNodeB-level traffic forecasting but also provides insights into anticipated eNodeBwise PRB utilization. The selected optimal deep learning model, BiLSTM, achieves a robust R2 score of 0.793, notably surpassing the performance of the other deep learning algorithms. Beyond the realm of PRB utilization, the study establishes a Quality of Service (QoS) threshold at 70% – a benchmark rooted in real network experience. This threshold serves as a pivotal trigger for decisions pertaining to soft parameter tuning. Leveraging the projected PRB utilization, the research introduces a pioneering algorithm designed to estimate eNodeB-level soft capacity parameter optimization. This algorithm empowers network operators to address short-term capacity enhancement solutions as well as long-term network expansion, all aimed at maintaining steadfast QoS benchmarks. Situated within the context of network planning, this study not only unravels the intricate dynamics of cellular data traffic but also catalyzes the concept of democratization. By harnessing the capabilities of deep learning, network operators are equipped with potent tools to navigate the intricate landscape of network optimization. Through this research endeavor, strides are made toward an envisioned future where technological advancements seamlessly converge with accessibility, thereby reshaping the contours of mobile network planning.