Master's Thesis Spiking Neural Networks in the Physical Layer of Wireless Communication Systems

25.02.2026, Studentische Hilfskräfte, Praktikantenstellen, Studienarbeiten

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In the design of wireless communication systems, models, such as channel models, have traditionally played a crucial role. However, these models always represent an idealized simplification and cannot fully capture all practical complexities. In contrast, data-driven approaches based on deep neural networks eliminate the need for explicit modeling and therefore have the potential to outperform classical designs. Current research explores deep learning techniques for tasks such as channel estimation, signal detection and modulation / demodulation, channel encoding and decoding, and physical-layer authentication.

Spiking neural networks (SNNs) are a novel type of neural network and differ fundamentally from conventional deep learning models by incorporating temporal dynamics and event-driven computation. SNNs operate using discrete spikes, similar to biological neurons, offering advantages in energy efficiency and temporal processing. Due to their event-driven, energy-efficient nature, SNNs are gaining attention in communications research.

This thesis focuses on designing and implementing SNNs to address selected physical-layer tasks, such as channel estimation. Initially, the performance of the implemented SNN will be evaluated through simulations of the SNN on GPUs. Subsequently, the network will be deployed on the best suitable specialized hardware platform ( such as SpiNNaker2, Pulsar, Loihi 2, Akida 2) to assess real-time performance under practical constraints, including latency and energy consumption. Furthermore, data from USRP X410 software-defined radios (SDRs) will be utilized to study the performance on real-world data.

This master’s thesis will be jointly supervised by the Neuromorphic Computing team at fortiss and the ACES Lab at the Chair of Theoretical Information Technology.

Work description:

▪ Literature review and collegial choice of the most appropriate task (channel estimation, signal detection, channel encoding, interference management) for an SNN implementation. Considered will be the maturity of the state-of-the-art, hardware constraints, hardware capabilities, and implementation complexity

▪ Design and implementation of an SNN (selecting a suitable neuron model, encoding scheme, and learning algorithm) for the selected task in software (CPU/GPU)

▪ Port and deployment of the SNN on neuromorphic hardware

▪ Performance evaluation (with synthetic and real-world data) and comparison to traditional approaches

Your qualifications:

▪ Knowledge of wireless communication systems

▪ Knowledge of neural networks and classical AI frameworks

▪ Programming experience in MATLAB and Python

▪ Experience with spiking neural networks (SNNs) and spiking frameworks is a plus

To apply:
Please send your application by email to Ullrich Mönich ( moenich@tum.de ) and over the fortiss application form at https://recruitment.fortiss.org/Unsolicited-application-eng-f11.html with the subject "Master’s Thesis: Neuromorphic Comm", including your CV and academic transcript.

Kontakt: moenich@tum.de