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Course: A unified simulation framework on deep learning for wireless communication course

So far, cellular networks and wireless standards have been designed based on approaches that have relied on domain experts using theoretical analysis or computer simulations to validate communication system building blocks. The building blocks consist of channel models, channel coding/decoding, precoding, detection, synchronization and channel estimation to name a few. MATLAB is a di facto simulation tool used for undergraduate courses such as GEI1056- Telecommunication systems and even by the research community.

On the other hand, deep learning (DL) is recognized as indispensable artificial intelligent (AI) tools for the design of future communication systems. ML can enable the learning of personalized waveforms, modulation schemes, and pilot sequences, which make more efficient use of the spectrum and are also optimally adapted to the practical limitations of the radio hardware and communication channel [1]. It can also enable emerging optimized signaling schemes and channel access procedures to smoothly transition between contention to schedule-based transmission modes depending on the environment and use cases. Learning a protocol can also address the problem of optimally multiplexing resources for sensing and communication. Python is a di facto programming tool for machine and deep learning.

The main objective is to devise a unified simulation framework on deep learning for wireless communication course based on MATHALB and Python to support the teaching quality of GEI1056 while introducing AI tools.

The approach will rely on (i) the Matlab’s communication tool box and (ii) NVIDIA’s Python based GPU-accelerated open-source library for link-level simulations that uses TensorFlow commonly known as Sionna.

The deliverable is comprehensive communication building blocks on Sionna in the form of lab assignments tutorials.

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