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arXiv Machine Learning By Aakash Saini, Jafar Aco Mohammadi, Wolfgang Gerstacker

On the Adversarial Vulnerability of Neural Receivers with Unrolled LDPC Decoders

Read the original on arXiv Machine Learning →

The paper investigates the adversarial vulnerability of neural receivers that incorporate unrolled LDPC decoders for 5G NR. It finds that standard gradient-based attacks are ineffective due to gradient masking caused by tanh saturation in the BP check-node update, leading to a dramatic decay in gradient norm over iterations. To overcome this, the authors propose two loss functions—L_BP and L_LLR—that restore attack efficacy, significantly increasing block-error rates across different neural receiver implementations.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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