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New research probes adversarial attacks on 5G neural receivers

By PulseAugur Editorial · [1 sources] ·

Researchers have investigated the adversarial vulnerability of neural receivers used in 5G New Radio (NR) systems. They found that standard gradient-based attacks are ineffective due to gradient masking caused by the tanh saturation in the belief-propagation (BP) update within unrolled low-density parity-check (LDPC) decoders. To address this, two new loss functions, L_BP and L_LLR, were proposed. L_BP uses per-iteration supervision to route gradients through unsaturated early iterations, while L_LLR applies a margin loss on pre-decoder log-likelihood ratios, bypassing the decoder entirely. Both methods significantly increase the block-error rate (BLER) under adversarial perturbations. AI

IMPACT This research could lead to more robust AI-driven communication systems, improving security against adversarial attacks.

RANK_REASON The cluster contains a research paper detailing a novel finding and proposed methods. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New research probes adversarial attacks on 5G neural receivers

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The cluster contains a research paper detailing a novel finding and proposed methods. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Aakash Saini, Jafar Aco Mohammadi, Wolfgang Gerstacker ·

    On the Adversarial Vulnerability of Neural Receivers with Unrolled LDPC Decoders

    arXiv:2610.30345v1 Announce Type: cross Abstract: Neural receivers for 5G New Radio (NR) match classical model-based receivers in link-level performance, but their robustness to adversarial perturbations at the receiver input remains underexplored. We show that standard gradient-…

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