arXiv (All)
open access
Radio Echo Representation Learning for Semantic Sensing in Multi-Modal ISAC Systems
Semantic sensing extends integrated sensing and communication (ISAC) beyond physical parameter estimation by exploiting task-oriented representations from radio echoes. Nevertheless, existing multimodal sensing remains largely limited to vision-assisted radio physical parameter estimation or typical
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- SemISAC: Semantic Integrated Sensing and Communications#1012498
- MNIST-PRO: MNIST is Back as a Partially Observable World for AI Agents#819055
- MNIST-PRO: MNIST is Back as a Partially Observable World for AI Agents#809063
- Semantic Split Inference for Remote Modulation Recognition#1217057
- Semantic Split Inference for Remote Modulation Recognition#1210243
Record · ID 1326588
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive