Zinkworks is a Member of the Ericsson Intelligent Automation Platform (EIAP) Ecosystem

Zinkworks is now a member of the Ericsson Intelligent Automation Platform (EIAP) Ecosystem, a pioneering initiative designed to accelerate the development of high-quality and high-value automation applications.

Ericsson Intelligent Automation Platform is Ericsson’s industry leading Service Management and Orchestration (SMO) offering. It includes a Non-Real-Time Radio Intelligent Controller (non-RTRIC) for running radio network applications (rApps) supported by an open software development toolkit (SDK) which can be used by communication service providers (CSPs), and third parties to develop new rApps. EIAP supports a vibrant ecosystem of rApps touching the complete lifecycle of network Automation. 

Zinkworks’ Prediction Platform offers a cutting-edge solution for prediction modelling, seamlessly integrating patented machine learning algorithms with intuitive APIs to deliver exceptional performance.

The platform features three powerful rApps:

• Network Position Prediction (NPP): Utilizes a framework to generate forecasts from positioning data, employing continuous training with MP-LSTM to predict user movements and improve network efficiency.
• Network Traffic Prediction (NTP): An advanced tool for predicting network traffic that considers spatial dependencies and non-linear temporal dynamics to adapt to changing network conditions.
• Quality of Service Prediction (QoS-P): An rApp for 5G networks that analyzes and predicts KPIs, integrating with orchestration apps to enhance 5G technology through adaptive profiling and streamlined operations.

Figure 1: Zinkworks NTP Radio Application Site Map

Figure 2: Zinkworks NTP Radio Application Downlink

Zinkworks’ rApps leverage the power of AI and machine learning to predict network behaviour and user trends, enabling CSPs to:

• Optimize Network Performance: Proactively predict and address network issues before they impact user experience.
• Reduce Energy Costs: Intelligently manage network resources and optimize traffic patterns to significantly decrease power consumption, cutting down on operational expenses.
• Improve Resource Allocation: Efficiently allocate network resources based on predicted traffic patterns.
• Enhance Service Quality: Deliver consistent and high-quality service to users by anticipating and addressing potential issues.