City:  Nürnberg
Date:  Sep 6, 2022

Master Thesis - Generalization of Channel Charting

The Fraunhofer-Gesellschaft (www.fraunhofer.com) currently operates 76 institutes and research institutions throughout Germany and is the world’s leading applied research organization. Around 30 000 employees work with an annual research budget of 2.9 billion euros. 

You are interested in bridging communication theory and machine learning and you think localization is exciting?

 

Motivation. The demand for increased traffic volume and number of terminals in future wireless communication systems requires the use of new technologies, e.g., massive multiple-input / multiple-output, millimeter-wave bands that rely on knowledge of transmitter locations to solve the challenges encountered in their practical application [1]. In contrast to supervised wireless location frameworks such as fingerprinting, Channel Charting (CC) is system-level unsupervised and operates directly on Channel State Information (CSI) that are passively collected at multi-antenna base stations (BS). As such, it avoids the expensive data collection phase of their supervised counterparts. While promising results in simulated, static environments have demonstrated the potential feasibility of the method [1, 2, 4], generalization and robustness in complex, dynamic environments with potentially sparse UE coverage is still an open question. Another unresolved challenge is the existing evaluation criteria of CC techniques, which essentially rely on qualitative inspection and thus suffer from a lack of well-calibrated and understood performance metrics. For example, trustworthiness (TW) and continuity (CT) impede the automatic comparison required for hyperparameter optimization. With the addition of side information, CC can be generated or mapped to reflect absolute receiver positions [3, 5].

 

Idea to address these challenges. Approaching the generalizability of CC should be used when evaluating state-of-the-art methods w.r.t. performance sensitivity to variations in parameters such as UE density, dynamic UEs, and LoS / NLoS components, ideally over a wide range of scenarios. Integrating time and velocity information from CC, as well as better CSI distance metrics, may help address generalization issues, so should be investigated. In addition, the extension of CC for absolute positioning with additional information should be observed. A user study should be conducted to calibrate the TW and CT metrics to better capture the quality of the resulting charts and competitors based on e.g. rank correlation can be proposed and compared.

 

What you will do

  • You transfer between different propagation environments and channels
  • You implement and evaluate state-of-the-art methods
  • You compare the robustness of the learned distance metrics for reconstructing geometrically structured channel diagrams
  • You define efficient application-specific metrics (beyond TW, CT, KST, and similarity metrics: euclidean distance vs triplet loss)
  • You adapt the CC framework to a simulation interface, such as QUADRIGA
  • You update CC to changing environments (dynamic objects, receivers and transmitters)
  • You will write an academic paper

 

What you bring to the table

  • You study Information Technology, Data Science or equivalent in a master program
  • You have programming experience with Machine Learning in Python
  • You hold preferably experience in working with common libraries such as numpy, pandas, matplotlib
  • You are ideally experienced with PyTorch or Tensorflow
  • You have preferably knowledge on radio communication

 

What you can expect

  • Open and friendly team work
  • Networking with scientists
  • Active contribution in applied research
  • Interesting an innovative projects
  • Mentoring program »josephine®« for talented female students

 

If you have any questions about this opening, please contact: Christian Nickel (christian.nickel@iis.fraunhofer.de).

 

The thesis will be assigned and carried out in accordance with the rules of your university. For this reason, please discuss the thesis with a professor who can advise you over the course of the project. The duration for the thesis should be 6 months.

 

We value and promote the diversity of our employees' skills and therefore welcome all applications - regardless of age, gender, nationality, ethnic and social origin, religion, ideology, disability, sexual orientation and identity. Severely disabled persons are given preference in the event of equal suitability. 

 

With its focus on developing key technologies that are vital for the future and enabling the commercial utilization of this work by business and industry, Fraunhofer plays a central role in the innovation process. As a pioneer and catalyst for groundbreaking developments and scientific excellence, Fraunhofer helps shape society now and in the future.

 

Interested? Apply online now (PDF: cover letter, CV, transcripts). We look forward to getting to know you!

 

Maximilian Müller

Fraunhofer Institute for Integrated Circuits IIS 

www.iis.fraunhofer.de 

 

Requisition Number: 29084                Application Deadline: None

 

References
[1] Studer, C., Medjkouh, S., Gonulta¸s, E., Goldstein, T., & Tirkkonen, O. (2018). Channel charting: Locating users within the radio environment using channel state information. IEEE Access, 6(?), pp. 47682-47698.
[2] Deng, J., Medjkouh, S., Malm, N., Tirkkonen, O., & Studer, C. (2018, October). Multipoint channel charting for wireless networks. In: Asilomar Conf. on Signals, Systems, and Computers (Asilomar), pp. 286-290.
[3] Lei, E., Casta˜neda, O., Tirkkonen, O., Goldstein, T., & Studer, C. (2019, September). Siamese neural networks for wireless positioning and channel charting. In: Annual Allerton Conf. on Communication, Control, and Computing (Allerton), pp. 200-207.
[4] Agostini, P., Utkovski, Z., & Sta´nczak, S. (2020, May). Channel charting: an Euclidean distance matrix completion perspective. In: Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP), pp. 5010-5014.
[5] Pihlajasalo, J., Koivisto, M., Talvitie, J., Ali-Loeytty, S., & Valkama, M. (2020). Absolute Positioning with Unsupervised Multipoint Channel Charting for 5G Networks. In IEEE Vehicular Technology Conference (VTC2020-Fall) (pp. 1-5). IEEE.

 


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