The paper introduces a lightweight joint source-channel coding framework for wireless image transmission that selectively replaces standard convolutional layers with depthwise separable convolutional layers. By varying the replacement ratio and position, the authors generate models with different computational complexities and evaluate how these changes affect reconstruction performance. Experiments show that replacing intermediate encoder and decoder layers yields a favorable complexity‑performance trade‑off, revealing redundancy in deep learning‑based JSCC systems and enabling substantial parameter reduction with only slight performance loss.
By Ming Ye, Kui Cai, Cunhua Pan, Zhen Mei, Wanting Yang, Chunguo Li
arXiv:2602. 11834v2 Announce Type: replace-cross Abstract: While machine learning (ML)-based receiver algorithms have received a great deal of attention in the recent literature, they often suffer from poor scaling with increasing spatial multiplexing order and lack of explainability and generalization.
By Mikko Honkala, Dani Korpi, Elias Raninen, Janne M. J. Huttunen
arXiv:2606. 31653v1 Announce Type: cross Abstract: Certified training aims to produce models whose predictions can be formally verified against adversarial perturbations, typically by optimising upper bounds on the worst-case loss over an allowed perturbation set.
By Matteo Melis, Jesus Martinez Del Rincon, Vishal Sharma
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 investigates the limits of the maximal coding rate reduction (MCR²) framework for out‑of‑distribution (OOD) generalisation. It shows that MCR² can lead to complete prediction failure under distribution shift, even when a perfectly stable feature is available, and that adding invariance principles from IRM or REx does not resolve this issue. The authors conclude that additional assumptions or learning principles are needed to guarantee stable OOD predictions with MCR².
By Menghui Zhou, Gaoshan Bi, Vitaveska Lanfranchi, Po Yang
arXiv:2607. 08717v1 Announce Type: new Abstract: Narrowband interference (NBI) severely degrades orthogonal frequency-division multiplexing (OFDM) systems by corrupting subcarriers and rendering classical soft demodulation ineffective.
By Emmanouil Kavvousanos, Francky Catthoor, Vassilis Paliouras