AI 中文总结
针对ISAC中开放无线信道带来的挑战，提出CoSMIC框架，通过语义旋转编码方案嵌入感知输出，设计整流流细化器重建语义表示，仿真验证该框架在隐蔽性约束下能提高语义重建质量并降低推理延迟。
AI 中文摘要
语义集成感知与通信（ISAC）被视为未来无线网络中实现高效智能连接的有前景范式。但开放无线信道使双功能波形易被检测，对隐蔽性、感知保真度和语义准确性的联合保障构成挑战。为此提出CoSMIC框架，通过语义调制将感知输出嵌入双功能ISAC波形。建立语义旋转编码方案，通过可微预算投影满足隐蔽性约束。分析雷达性能，设计可靠性引导的整流流细化器从粗略观测中有效重建高保真语义表示。仿真结果表明，CoSMIC在严格隐蔽性约束下，相比基于扩散 的基线方案，语义重建质量提高18%，推理延迟大幅降低，验证了其在实际ISAC场景中的适用性。
英文摘要
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.
Comments13pages, 10figures. Submitted to IEEE TWC
