The Chair of Media Technology at the Technical University of Munich (TUM) is offering an exciting Master’s Thesis opportunity in the context of future 6G networks and digital twin-based optimization. The thesis will contribute to a cutting-edge research project focused on the planning and simulation of 6G wireless systems in complex environments.

The topic is split into the following steps.

Selection and Evaluation of Simulation software

As 6G networks are still under development, simulation is essential for validating novel methods. The first part of this thesis involves identifying and evaluating suitable 6G simulation platforms that can replicate key network functions and ray-tracing-based propagation models. The selected simulator should ideally support:

• 3D point clouds or triangular mesh input

• CAD data processing

• Modifiable object classes

• Material-aware modeling

Candidate simulators:

• DeepMIMO, https://www.deepmimo.net/, Letzter Zugriff Februar 2025
• NIST, https://github.com/usnistgov/qd-realization, Letzter Zugriff Februar 2025
• Hoydis, Jakob, et al. "Sionna: An open-source library for next-generation physical layer research." arXiv preprint arXiv:2203.11854 (2022).

Offline Optimization for 6G Network Deployment

Building upon the simulator selected above, the second part focuses on offline optimization of access point placement and beamforming strategies using a digital twin of the environment. This involves:

• Differentiating between static and dynamic objects (e.g., fixed machinery or columns)

• Incorporating material-specific signal propagation effects

• Developing methods to maximize signal coverage by optimizing AP locations in a virtual replica of the environment

Voraussetzungen

3D Gaussian Splatting (3DGS) has demonstrated strong performance in high-quality novel view synthesis and real-time rendering by representing scenes as dense sets of Gaussians with associated attributes [1]. This representation captures fine geometric detail and view-dependent appearance efficiently at render time, contributing to its practical success. However, trained 3DGS models often contain millions of Gaussians, resulting in substantial storage and memory requirements that limit scalability and deployment on resource-constrained systems.

This project focuses on exploiting geometric relationships among Gaussians (i.e., primitives) to reduce redundancy in the attribute space. The goal is to apply sampling and sparsification techniques to decrease the number of stored primitives while preserving perceptual and structural fidelity. The proposed methodologies will be evaluated against existing compression approaches [2]. The project aims to disseminate the results in highly recognized scientific venues; hence, the applicants should be motivated to do research.

[1] Kerbl, Bernhard, et al. "3d gaussian splatting for real-time radiance field rendering." ACM Trans. Graph. 42.4. 2023.

[2] Bagdasarian, Milena T., et al. "3dgs. zip: A survey on 3d gaussian splatting compression methods." Computer Graphics Forum. Vol. 44. No. 2. 2025.

Voraussetzungen