PyJama: Differentiable Jamming and Anti-Jamming with NVIDIA Sionna
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910537096364032 |
|---|---|
| author | Ulbricht, Fabian Marti, Gian Wiesmayr, Reinhard Studer, Christoph |
| author_facet | Ulbricht, Fabian Marti, Gian Wiesmayr, Reinhard Studer, Christoph |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_15473 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | PyJama: Differentiable Jamming and Anti-Jamming with NVIDIA Sionna Ulbricht, Fabian Marti, Gian Wiesmayr, Reinhard Studer, Christoph Signal Processing Information Theory 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. |
| title | PyJama: Differentiable Jamming and Anti-Jamming with NVIDIA Sionna |
| topic | Signal Processing Information Theory |
| url | https://arxiv.org/abs/2407.15473 |