SemISAC：语义集成感知与通信

中文摘要 AI 辅助

传统的集成感知与通信（ISAC）系统主要通过共享物理资源来集成通信与感知功能，并未显式利用与任务相关的语义信息。为超越这种物理层面的集成，我们提出了语义集成感知与通信（SemISAC），这是一个统一框架，将语义通信（SemCom）与语义感知（SemS）相结合，以传递信源含义并获取环境含义。具体而言，发射端将信源语义和感知任务信息与可用的辅助信息相结合，以设计共享波形并分配无线电资源，而接收端的通信与感知任务解码器则分别恢复信源含义并推断所需的环境信息。我们还提供了一种信息论解释，以刻画物理信息与任务相关信息之间的关系以及SemISAC中由此产生的语义权衡。在此框架基础上，我们构建了通用的SemISAC设计问题，并提出了两种实现方法，即端到端（E2E）SemISAC优化和模块化SemISAC优化。作为具体实现，我们将模块化SemISAC优化应用于正交频分复用（OFDM）系统中可学习时频（TF）预编码器的联合设计，以执行代表性的SemCom和SemS任务。仿真结果表明，所提出的实现方案在满足给定通信要求的情况下降低了感知语义失真，并实现了比基线设计更优的通信-感知权衡。

英文摘要

Conventional integrated sensing and communications (ISAC) systems primarily integrate communications and sensing through shared physical resources, without explicitly exploiting task-relevant semantic information. To move beyond such physical-level integration, we propose semantic ISAC (SemISAC), a general framework that unifies semantic communication (SemCom) and semantic sensing (SemS) to convey source meaning and acquire environmental meaning. Specifically, the transmitter combines source semantics and sensing task information with available side information to design the shared waveform and allocate radio resources, while the receiver-side communication and sensing task decoders recover the source meaning and infer the required environmental information, respectively. We also provide an information-theoretic interpretation to characterize the relationship between physical and task-relevant information and the resulting semantic trade-off in SemISAC. Building on this framework, we formulate the general SemISAC design problem and propose two realization methods, namely end-to-end (E2E) SemISAC optimization and modular SemISAC optimization. As a concrete realization, we apply modular SemISAC optimization to jointly design a learnable time-frequency (TF) precoder in an orthogonal frequency-division multiplexing (OFDM) system for representative SemCom and SemS tasks. Simulation results demonstrate that the proposed realization reduces sensing semantic distortion under a given communication requirement and achieves a more favorable communication-sensing trade-off than baseline designs.

发表机构

• University of Technology Sydney（悉尼科技大学）
• La Trobe University（乐卓博大学）
• Monash University（蒙纳士大学）
• Technical University of Berlin（柏林工业大学）
• Imperial College London（帝国理工学院）

机构由 AI 辅助整理，请以论文原文为准。