In the rapidly evolving world of 5G technology, where network demands are constantly increasing, accurate prediction of network traffic has become a critical element for efficient network management, especially in O-RAN. To address this challenge, Zinkworks has introduced a solution that aims to revolutionise network traffic prediction and management within the 5G core network. This blog showcases the significance of Network Traffic Prediction (NTP) and its profound implications for the telecommunications industry.

Use Case Overview 

Network traffic is the primary carrier of data transmission within modern telecommunications networks. It comprises of packets that carry most of the network’s load. With the introduction of 5G technology, there is a growing need for intelligent prediction of cellular traffic loads. Being able to predict the number of packets per second (PPS) or bytes per second is crucial for optimising network operations, especially within the 5G core network.

Solution Aim 

Our main goal is to implement a machine learning model that is both scalable and centralised and hosted on the RAN Intelligent Controller (RIC). This model will enable us to conduct proactive traffic analysis and load prediction for thousands of connected Open Radio Units (O-RUs) within the cluster. Our key objectives include scalability, centrality, adherence to MLOps principles, and meeting time-constrained inference processes. 


Methodology Overview 

The NTP model is a cutting-edge solution that leverages a sophisticated methodology to encapsulate graph transformation and Deep-Learning ML models. It adeptly captures temporal, spatiotemporal, and dynamic correlations between network elements, providing businesses with unparalleled insights into complex network structures. With the NTP model, Network orchestrator can gain a competitive edge and confidently make data-driven decisions, all while maintaining the utmost security and confidentiality. 


Modules Description 

  1. Graph Transformation

The initial phase involves transforming the network into a graph representation. Site coordinates morph into graph nodes, while edges derive from site profiles and azimuths of local antennas. Historical traffic loads and additional parameters are encoded as node characteristics, with edge weights mirroring geographical distances and handover occurrences. The resulting adjacency matrix optimally normalises features for computation. 

  1. Model Designing and Training

NTP integrates a robust deep learning model with automated optimisation processes to improve accuracy. This includes fine-tuning layer size adjustments, selecting activation functions, and optimising learning rates. The resultant Docker version is seamlessly deployable within the cluster and integrates effortlessly with other services via REST API. 


The Results 

Our cutting-edge solution is revolutionising traffic prediction with its adaptive accuracy, providing users with the ability to forecast traffic load with an unparalleled level of precision. The model utilises a threshold set by the user to predict traffic, and our initial assessments show that it can achieve up to 80% accuracy for predicting traffic load one day ahead. With shorter prediction windows, the accuracy potential soars to 92.5%, forecasting just minutes ahead of time. Our solution is the perfect tool for any 5G orchestrator requiring accurate traffic prediction, giving network operators the confidence to make informed decisions based on real-time data. 



Introducing Zinkworks’ NTP framework – a network traffic prediction for 5G core networks. With cutting-edge machine learning techniques at its core, NTP offers unparalleled scalability, centrality, and real-time insights vital for optimising network operations in the 5G era. NTP’s transformative impact on the telecommunications industry is undeniable, ushering in a new era of efficiency and reliability. With NTP, telecommunication service providers can experience a paradigm shift in network traffic prediction, resulting in a superior user experience, reduced downtime, and improved overall network performance.  

If you would like to learn more about NTP please contact our team, or arrange to speak with us at MWC, click here.