The paper proposes a machine‑learning–based wireless image transmission scheme that merges compression and channel coding via a multi‑view autoencoder, enabling multiple transmitters to share the same channel resources simultaneously in a non‑orthogonal multiple‑access (NOMA) setting. A progressive fine‑tuning algorithm doubles the number of users at each iteration while preserving orthogonalized performance, allowing the system to support up to 64 users with only a 2.4 % increase in trainable parameters. The method improves recovered image quality and outperforms existing NOMA‑based approaches across various datasets, metrics, and channel conditions.
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.
The paper presents a deep‑learning‑based end‑to‑end multi‑user communication design for dense IoT networks operating under interference‑limited, finite‑blocklength conditions. It extends a 2‑user SiameseNet transceiver to support 2, 4, and 8 users, using learned redundancy to suppress interference and improve noise robustness without joint detection. Preliminary results also show potential for a 2×2 MIMO configuration with fixed‑channel CSIT and CSIR.
arXiv:2606. 12858v1 Announce Type: cross Abstract: Conventional communication systems, including both separation-based coding and learning-based joint source-channel coding (JSCC), are typically designed under Shannon's rate-distortion theory.
arXiv:2505. 10946v3 Announce Type: replace-cross Abstract: Token communications (TokenCom) is an emerging generative semantic communication paradigm, where tokens serve as compact representation units across modalities.
The paper introduces SemQAM, a joint semantic-channel coding and modulation framework for generative image transmission that converts source representations directly into modulated QAM symbols. By vector‑quantizing latent features and mapping each index to a QAM symbol, SemQAM allows channel impairments to influence codebook learning and employs conditional expectations of received codewords to mitigate error propagation in autoregressive decoding. Experiments demonstrate that SemQAM-C matches or surpasses conventional source coding methods while reducing channel uses by 18.57% compared to separated schemes, highlighting its efficiency and effectiveness in joint coding.
arXiv:2604.02691v2 Announce Type: replace Abstract: Deep learning based semantic communication has achieved significant progress in wireless image transmission, but most existing schemes rely on fixe...
arXiv:2602. 12338v2 Announce Type: replace Abstract: Token Communications (TokenCom) has recently emerged as an effective new paradigm, where tokens are the unified units of multimodal communications and computations, enabling efficient digital semantic- and goal-oriented communications in future wireless networks.
Learned image compression (LIC) has achieved impressive rate-distortion performance. However, existing methods remain highly vulnerable to packet loss, a common challenge in satellite and emergency communications.
arXiv:2607. 01921v1 Announce Type: cross Abstract: Communication systems designed for reliable data reconstruction, rather than task-oriented communication, typically rely on separate source and channel coding and incur high latency under limited spectrum availability and fading channels.
Digital image transmission typically optimizes source coding, channel coding, and modulation separately, limiting how source representations adapt to channel impairments. We propose SemQAM, a joint se...
The paper introduces KDG‑SemNOMA, a framework for 6G robotic vehicle networks that combines knowledge distillation and generative models to improve semantic communication over uplink non‑orthogonal multiple access (NOMA). It employs a ConvNeXt‑based deep joint source‑channel coding architecture with an enhanced attention feature module for dynamic channel adaptation, and uses an orthogonal teacher model to guide a NOMA student model via two‑stage knowledge distillation. A channel‑conditional GAN further refines the reconstructed images, yielding higher pixel‑level accuracy and perceptual fidelity on the FFHQ‑256 dataset compared to state‑of‑the‑art methods.
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.