Master Thesis: Digital Twin Model for SFP+, SFP28 and SFP56 Optical Transceivers

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About this opportunity:

Optical transceivers SFP are critical building blocks in modern telecommunication networks. Their performance is affected by environmental conditions, temperature, aging, optical link quality, and operational stress factors. Predicting module behaviour and potential failures before they occur is becoming increasingly important for maintaining network reliability and reducing operational costs.

This thesis aims to investigate and develop a Digital Twin model for optical transceivers. The digital twin will be trained using data collected from deployed modules and laboratory measurements to create a virtual representation of the physical device. The model will be used to predict module behaviour under various operating conditions, including extreme temperatures, low received optical power levels, and other stress scenarios.

The research will explore how Artificial Intelligence (AI), Machine Learning (ML), and data analytics can be applied to improve fault prediction, performance monitoring, and proactive maintenance of optical communication systems.

What you will do:

• Study the architecture, diagnostics, and management interfaces of SFP+, SFP28, and SFP56 optical transceivers.


• Collect, analyze, and preprocess operational data from real modules and laboratory measurements.


• Design and implement a Digital Twin framework representing the behaviour of optical modules.


• Develop machine learning models capable of predicting module performance under different operating conditions.


• Simulate scenarios such as high temperature operation, extreme temperature exposure, low Rx optical power conditions and aging and performance degradation.


• Investigate anomaly detection methods for identifying abnormal module behaviour.


• Develop predictive maintenance algorithms capable of forecasting potential failures before service impact occurs.


• Validate the model using real-world measurements and compare predictions against actual module performance.
  
  

The skills you bring:

• We are looking for students pursuing a Master’s degree in Computer Science, Software Engineering, Data Science, Machine Learning or Artificial Intelligence.


•  A background of Photonics, Optical Communication Engineering, Electrical Engineering or Telecommunications Engineering is considered a plus.


• Knowledge about SFP+, SFP28, SFP56, QSFP, or related technologies is a merit.


• Data analytics and statistical modelling.


• Digital Twin concepts and simulation methodologies


• Ability to work independently while collaborating with experts