The paper presents a method for jointly training an ensemble of weighted belief‑propagation decoders by optimizing trainable Tanner‑graph edge weights. Four ensemble objectives are compared, and best‑member training is found to be most effective. On a (64,32) PEG LDPC code, the ensemble achieves a 0.66 dB gain over a single decoder at FER = 10⁻⁴, and similar improvements are seen on a (128,64) CCSDS LDPC code, demonstrating learned complementary failure events rather than uniformly better components.
By Felix Krieg, Paul Bezner, Stephan ten Brink
arXiv:2609.00715v1 Announce Type: cross
Abstract: Feedback-based coding schemes have demonstrated substantial performance gains over today's open-loop coding schemes. Unfortunately, these gains are u...
By Jonggyu Jang, Hongjae Nam, Vishrant Tripathi, David J. Love, Hyun Jong Yang
arXiv:2605.10681v2 Announce Type: replace-cross
Abstract: Forward error correction is essential for reliable communication over noisy channels. Attention-based model-free neural decoders have shown s...
By Rostislav Gusev, Nikita Aleksandrov, Artem Solomkin, Dmitry Artemasov
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.
By Aakash Saini, Jafar Aco Mohammadi, Wolfgang Gerstacker
The paper demonstrates that incorporating code automorphisms into syndrome-based neural decoding (SBND) improves the models’ learning and generalization through data augmentation during training and inference. By applying this technique to short, high-rate codes, the authors achieve performance close to maximum likelihood decoding (MLD) using small datasets and appropriate training. The study also indicates that previous SBND results may have underestimated their true error‑correction capability due to insufficient training.
By Rapha\"el Le Bidan, Ahmad Ismail, Elsa Dupraz, Charbel Abdel Nour
arXiv:2608. 13961v1 Announce Type: new Abstract: Federated learning (FL) enables collaborative model training across distributed devices without sharing raw data; however, it faces significant communication bottlenecks and channel impairments in practice.
By Han Xiao, Wei Kang, Nan Liu
arXiv:2509. 16577v2 Announce Type: replace Abstract: Over-the-air (OTA) aggregation enables federated edge learning (FEEL) by exploiting the superposition property of the wireless channel to merge communication with computation, eliminating the need to schedule and decode devices individually.
By Antonio Tarizzo, Mohammad Kazemi, Deniz G\"und\"uz
The paper introduces an asynchronous message‑passing framework for Graph Neural Networks to mitigate oversquashing, a problem where distant nodes cannot effectively communicate due to structural bottlenecks. Unlike conventional synchronous updates, the method updates a centrality‑guided batch of nodes at each layer, allowing information to propagate sequentially and reducing the need for increased channel capacity. Experiments on six standard and two long‑range graph classification benchmarks show notable performance gains, including 5 % improvement on REDDIT‑BINARY and 4 % on Peptides‑struct.
By Kushal Bose, Swagatam Das
arXiv:2506. 01260v2 Announce Type: replace Abstract: Scaling models has led to significant advancements in deep learning, but training these models in decentralized settings remains challenging due to communication bottlenecks.
By Sameera Ramasinghe, Thalaiyasingam Ajanthan, Gil Avraham, Yan Zuo, Alexander Long
The paper introduces the first bound‑propagation framework for verifying the decoder of Deep Joint Source‑Channel Coding (DeepJSCC), providing worst‑case reconstruction error guarantees over a specified wireless channel noise region. It extends existing DNN verification techniques to handle PReLU activations, transposed convolutions, and Rayleigh fading, and incorporates Lipschitz‑regularized global robustness training (GloRo) to tighten certification bounds. Experiments on image transmission models show significant improvements in certified safety and real‑world OFDM validation confirms the theoretical guarantees.
By Thanh Le, Hai Duong, Takeshi Matsumura, ThanhVu Nguyen
arXiv:2607.11577v2 Announce Type: replace-cross
Abstract: Conventional Graph Neural Networks (GNNs) couple feature transformation and neighborhood aggregation, which often renders them vulnerable to...
By Chengcheng Yan, Feifei Zhao, Dai Zhu, Wei Liu, Qingsong Wang
arXiv:2506. 01260v3 Announce Type: replace Abstract: Scaling models has led to significant advancements in deep learning, but training these models in decentralized settings remains challenging due to communication bottlenecks.
By Sameera Ramasinghe, Thalaiyasingam Ajanthan, Gil Avraham, Yan Zuo, Alexander Long