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: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.
arXiv:2607. 20666v1 Announce Type: cross Abstract: The robustness of machine learning techniques across heterogeneous network domains remains an open challenge in optical 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:2609.39296v1 Announce Type: new Abstract: Video semantic communication has attracted increasing attention as a promising approach to improving video transmission efficiency. However, most exist...
arXiv:2609.14616v1 Announce Type: cross Abstract: Semantic image communication seeks to preserve task-relevant scene structure under limited channel resources, but carriers are often dense latent ten...
The paper introduces a semantic communication approach for CSI feedback in FDD massive MIMO, where the UE sends a learned embedding optimized for beam selection rather than reconstructing the full channel. Experiments show that an 8‑dimensional embedding derived from just 43 NR CSI‑RS pilots in the angular‑delay domain achieves the best beam prediction accuracy, surpassing methods that use the entire 512‑subcarrier channel. This demonstrates that beam‑relevant information is inherently low‑dimensional, allowing the semantic encoder to discard irrelevant details and transmit only the intent needed for beam selection.
arXiv:2607. 05419v1 Announce Type: cross Abstract: Accurate and timely channel state information (CSI) is essential for next-generation wireless systems, yet existing works treat CSI compression and CSI prediction as separate problems, both in academia and in current 3GPP studies.
The paper introduces FMSGOC, a framework that leverages visual‑linguistic foundation models to improve semantic and goal‑oriented communication for 6G. By transmitting a sparse set of semantic anchors and using a pretrained diffusion model for masked completion, it reduces overfitting and achieves high rate efficiency, reaching 0.039 BPP while maintaining strong semantic fidelity and robustness on unseen data.
arXiv:2606. 31303v1 Announce Type: cross Abstract: The emerging techniques of semantic communications and edge computing in 6G networks necessitate a paradigm shift toward co-designed semantic-aware and adaptive resource allocation for short-packet transmissions.
arXiv:2501. 16726v2 Announce Type: replace-cross Abstract: Semantic communications aim to enhance transmission efficiency by jointly optimizing source coding, channel coding, and modulation.
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.