Empowering mobile network planning through deep learning: a path to democratization
View/ Open
Date
2023-09Publisher
Brac UniversityAuthor
Nabi, Syed TauhidunMetadata
Show full item recordAbstract
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expansion decisions based on outdated methodologies.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.