Internship: Machine Learning for Interference rejection in Body Area Networks

Leuven

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NXP Semiconductors

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Motivation

There are several wireless communication technologies that can be used to create a Wireless Body Area Network (WBAN) and they could operate at different frequency bands. Some examples of this are at very low frequencies around 10MHz and other examples operate at ISM bands like 864MHz, 2.4Ghz etc.

Irrespective of which frequency bank WBAN operates, unwanted interfering signals can significantly degrade the communication link performance of a WBAN. Finding methods to mitigate these interferers is essential to ensure a robust communication link

Work

Signal separation or interference mitigation will help in extracting the required signal of interest and thereby improving the fidelity and performance of the communication link. Source separation activity using Machine Learning (ML) techniques have demonstrated signification performance improvements in other domains link vision, audio, etc.

The goal here is to apply machine learning techniques to  RF signals, where the wanted signal of interest and any unwanted signals are identified and separated. The work will involve setting up a ML environment to train a ML model with database(s) comprising of wanted communication signals along with relevant interference signals. The student should also study / look into wider ML challenges taking place in this domain, where training data bases are available. This can be a good starting point to setup the ML environment and start the investigation of algorithms.

This work is planned for around 5 months full-time internship at NXP.

Deliverable

The result of the work will be to come up with a ML framework / environment and the required algorithms, ML models that will demonstrate interference rejection capability in a WBAN communication link.

Profile of the student :

You study as master in electrical engineering, nano-technology or information technology.  You are interested in wireless communication, machine learning, and have notions of ML frameworks like Tensorflow, Pytorch, Matlab etc. You are creative, hands-on and have good communication and English language skills.

Work environment :

You have the opportunity to work in a fully equipped high tech environment. You will be coached by experienced engineers. Your location is mainly NXP Leuven.

Your contact person :

Antony Joseph, System Architect (antony.joseph@nxp.com)

More information about NXP in Belgium...

Tags: Engineering Machine Learning Matlab ML models PyTorch TensorFlow

Perks/benefits: Startup environment

Region: Europe
Country: Belgium
Job stats:  38  10  0

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