Paper on Differentiable Jamming and Anti-Jamming in NVIDIA Sionna Accepted at SPAWC 2024
A preprint is available on arXiv!
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, the IIP Group releases PyJama, a fully-differentiable open-source library that adds jamming and anti-jamming functionality to NVIDIA Sionna.
The accompanying paper demonstrates the utility of PyJama (i) for realistic MIMO simulations by showing examples that involve forward error correction, OFDM waveforms in time and frequency domain, 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.
PyJama has been developed by Fabian Ulbricht during his master thesis at the IIP Group, during which he was supervised by Gian Marti and Reinhard Wiesmayr. The paper is co-authored by Fabian Ulbricht, Gian Marti, Reinhard Wiesmayr, and Prof. Christoph Studer. A preprint is available on external page arXiv, and the code is available on external page GitHub. Also check out the PyJama project website!