面向ISAC中统一语义通信与语义感知的自适应导频选择

中文摘要 AI 辅助

语义通信（SemCom）与通感一体化（ISAC）是未来6G无线网络中的两项有前景的技术。现有研究已将语义技术应用于ISAC的通信模块或感知模块。在这项工作中，我们提出了SemISAC，它在单一双功能波形中同时执行语义通信和语义感知。SemISAC使用一个联合语义编码器，为通信和感知提取任务特定信息。我们在一个车辆场景中评估SemISAC，其中车辆共享道路环境的像素级分割，并通过感知对周围物体进行分类并估计其距离。在发射端，一个深度学习编码器将输入的道路场景图像转换为语义符号，并将其放置在OFDM网格的数据单元上，而其余单元则用作信道状态信息估计和感知的导频。导频配置根据信道条件自适应优化，以平衡通信和感知需求。在接收端，一个深度学习模型从接收波形中重建分割结果，而发射车辆捕获来自周围物体的反射波形，并使用任务特定的深度学习解码器进行目标识别和距离估计。仿真结果表明，SemISAC实现了与专用语义通信模块相近的分割精度，同时在目标识别和距离估计方面优于传统基线和语义基线。

英文摘要

Semantic communication (SemCom) and integrated sensing and communication (ISAC) are promising technologies for future 6G wireless networks. Existing studies have applied semantic technology to either the communication module or the sensing module of ISAC. In this work, we propose SemISAC, which performs both SemCom and semantic sensing within a single dual-function waveform. SemISAC uses a joint semantic encoder that extracts task-specific information for both communication and sensing. We evaluate SemISAC in a vehicular scenario in which vehicles share pixel-wise segmentation of the road environment and, through sensing, classify surrounding objects and estimate their ranges. On the transmitter side, a deep learning encoder converts the input road-scene image into semantic symbols and places them on the data cells of an OFDM grid, while the remaining cells serve as pilots for channel state information estimation and sensing. The pilot configuration is adaptively optimized based on the channel conditions to balance communication and sensing requirements. At the receiver, a deep learning model reconstructs the segmentation from the received waveform, while the transmitting vehicle captures the reflected waveforms from surrounding objects and uses task-specific deep learning decoders for target recognition and range estimation. Simulation results show that SemISAC achieves a segmentation accuracy close to that of the dedicated SemCom module while outperforming both conventional and semantic baselines in target recognition and range estimation.

发表机构

• National University of Sciences and Technology（国立科技大学）
• Kyung Hee University（庆熙大学）

机构由 AI 辅助整理，请以论文原文为准。