Covert Semantic Transmission in ISAC: Dual-Functional Waveform Design and Rectified Flow-Assisted Recovery

2026-07-28

AI Summary

This work addresses the challenge in semantic Integrated Sensing and Communication (ISAC) that existing dual-functional waveforms struggle to simultaneously achieve covertness, sensing fidelity, and semantic accuracy. To this end, the authors propose the CoSMIC framework, which embeds sensing outputs into the waveform via semantic modulation, employs semantic rotational coding to satisfy covertness constraints, and introduces a reliability-guided Rectified Flow (RFlow) refiner to reconstruct high-fidelity semantic representations. This study presents the first semantic ISAC system explicitly designed for covertness, demonstrating an 18% improvement in semantic reconstruction quality over diffusion-model baselines while preserving the radar mainlobe response and maintaining bounded signal-to-interference-plus-noise ratio (SINR). The approach also significantly reduces inference latency, confirming its effectiveness and practicality in real-world scenarios.

Abstract

Semantic integrated sensing and communication (ISAC) is envisioned as a promising paradigm for efficient and intelligent connectivity in future wireless networks. However, the open wireless channel exposes the dual-functional waveform to detection, which challenges the joint guarantee of covertness, sensing fidelity, and semantic accuracy. To address the challenge, we propose CoSMIC, a novel covertness-oriented semantic ISAC framework, where the sensing output is embedded into a dual-functional ISAC waveform through semantic modulation. Specifically, a semantic rotation coding scheme is established to map semantic latents onto the pairwise rotation and scaling of Gaussian reference sequences, which satisfies a derived closed-form covertness constraint by a differentiable budget projection. Moreover, the radar performance is analyzed to confirm an invariant matched-filter mainlobe response and a bounded output signal-to-interference-plus-noise ratio (SINR) under the semantic embedding. Subsequently, a reliability-guided rectified flow (RFlow) refiner is designed to effectively reconstruct high-fidelity semantic representations from coarse observations. Simulation results demonstrate that CoSMIC improves the semantic reconstruction quality by 18% over diffusion-based baseline schemes with substantially reduced inference latency under strict covertness constraints, which validates the applicability to practical ISAC scenarios. The source code and video demonstrations are available at https://github.com/LanceAnlan/CoSMIC-covertness-oriented-semantic-ISAC-framework.

Problem

• covertness
• semantic ISAC
• dual-functional waveform
• semantic accuracy
• sensing fidelity

Innovation

• semantic ISAC
• covertness
• dual-functional waveform
• rectified flow
• semantic modulation