Scope:

NFC-enabled mobile devices that operate in card-emulation mode typically use active load modulation (ALM) for communication with the reader. ALM refers to actively emitting an RF signal instead of passively modulating the reader RF field by switching passive components, referred to as passive load modulation. Correct phase alignment of the NFC reader field and the ALM signal is crucial for successful communication. To find a transmission (TX) phase configuration that maximizes the communication success rate across all relevant scenarios, currently, exhaustive measurement campaigns have to be performed. These campaigns typically take several weeks, which is undesired.

The aim of the Master Thesis proposed in this document is to develop a tool to reduce the measurement effort while still being able to accurately characterize the TX phase impact on communication success rate by applying artificial neural network (ANN)-based models (e.g. CNN, GAN)

Prior work has been conducted on a similar dataset and the student can use this work as a starting point for database creation and model generation.

Objectives:

• Find models that predict full communication success rate characterization as function of TX phase from an incomplete set of measurements.
• Find the minimum set of measurements that is required to perform accurate communication success rate prediction.
• Incorporate the model from (1) into a tool that predicts the communication success rate characterization for arbitrary reader devices based on its inputs.

Your Profile:

• Master student in electrical engineering or similar studies
• Basic knowledge of digital signal processing and NFC communication systems
• Good communication skills in English
• Experience with ANNs, Tensorflow, and Python is an advantage

If you would are interested in this master thesis topic, apply online!

For the successful completion of the Master Thesis you will receive a remuneration of a one-time payment of EUR 5.250, - gross. Additionally you will get a bonus of EUR 2.000, - gross if you receive an excellent grade “Sehr gut” for your Thesis!