PyJama: Differentiable Jamming and Anti-Jamming with NVIDIA Sionna
<|reference_start|>PyJama: Differentiable Jamming and Anti-Jamming with NVIDIA Sionna: Despite extensive research on jamming attacks on wireless communication systems, the potential of machine learning for amplifying the threat of such attacks, or our ability to mitigate them, remains largely untapped. A key obstacle to such research has been the absence of a suitable framework. To resolve this obstacle, we release PyJama, a fully-differentiable open-source library that adds jamming and anti-jamming functionality to NVIDIA Sionna. We demonstrate the utility of PyJama (i) for realistic MIMO simulations by showing examples that involve forward error correction, OFDM waveforms in time and frequency, realistic channel models, and mobility; and (ii) for learning to jam. Specifically, we use stochastic gradient descent to optimize jamming power allocation over an OFDM resource grid. The learned strategies are non-trivial, intelligible, and effective.<|reference_end|>
@article{ulbricht2024pyjama:,
    title={PyJama: Differentiable Jamming and Anti-Jamming with NVIDIA Sionna},
    author={Fabian Ulbricht and Gian Marti and Reinhard Wiesmayr and Christoph
  Studer},
    journal={arXiv preprint arXiv:2407.15473},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.15473},
    primaryClass={eess.SP cs.IT math.IT}
}