AI 中文总结
针对双站集成感知与语义通信系统，本文提出SA-ASCC与波束成形设计框架，采用AO算法优化问题，性能优于DJSCC-WF-ZF等基准方案。
AI 中文摘要
语义通信（SemCom）作为6G时代提升集成感知与通信系统性能的新范式，通过传输任务相关的语义特征而非原始比特，具备提升传输效率的潜力。然而，现有研究多聚焦于感知数据压缩以降低后续通信开销，未考虑语义通信与感知任务的集成传输框架。本文针对双站集成感知与语义通信（ISSC）系统，提出感知感知型自适应信源信道编码（SA-ASCC）与波束成形设计框架，联合优化语义通信任务的编码速率及语义通信与感知任务的发射波束成形。具体而言，本文通过推导由信源与信道编码诱导分量构成的上界，近似得到端到端语义失真函数；针对所考虑的双站ISSC系统中收发分置导致的非理想时间同步下的目标位置，推导混合克拉美罗界（HCRB）。为表征语义通信与感知性能间的可达区域，本文考虑HCRB阈值、信道使用次数及功率预算，构建失真最小化问题；该问题因耦合设计变量与混合整数规划而具有非凸性，本文提出交替优化（AO）算法，将其分解为模型选择、联合速率与波束成形优化子问题，分别采用穷搜法及连续凸近似与分式规划结合的方法求解。仿真结果表明，所提方案性能优于DJSCC-WF-ZF与BPG-WF-ZF基准方案。
英文摘要
Semantic communication (SemCom) has emerged as a new paradigm to facilitate the performance of integrated sensing and communication systems in 6G, due to its potential to enhance transmission efficiency by transmitting task-relevant semantic features rather than raw bits. However, most of the existing works mainly focus on sensing data compression to reduce the subsequent communication overheads, without considering the integrated transmission framework for both the SemCom and sensing tasks. This paper proposes a sensing-aware adaptive source-channel coding (SA-ASCC) and beamforming design framework for bi-static integrated sensing and SemCom (ISSC) systems by jointly optimizing the coding rate for SemCom task and the transmit beamforming for both the SemCom and sensing tasks. Specifically, an end-to-end semantic distortion function is approximated by deriving an upper bound composing of source and channel coding induced components, and then a hybrid Cramér-Rao bound (HCRB) is derived for target position under imperfect time synchronization due to the transceiver deployed at different places in our considered bi-static ISSC system. To characterize the achievable region between SemCom and sensing performance, a distortion minimization problem is formulated by considering the HCRB threshold, channel uses, and power budget, which is non-convex due to the coupled design variables and the mixed-integer program. Subsequently, an alternating optimization (AO) algorithm is proposed to decompose this problem into the model selection and joint rate and beamforming optimization subproblems, which are solved by the exhaustive search method and the combination of successive convex approximation and fractional programming, respectively. Finally, simulation results demonstrate that the proposed scheme outperforms the DJSCC-WF-ZF and BPG-WF-ZF benchmarks.
