OFDM-ISAC系统中几何与概率星座成形的联合优化
Joint Optimization of Geometric and Probabilistic Constellation Shaping for OFDM-ISAC Systems
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中文总结 AI 辅助
针对OFDM-ISAC系统，利用自动编码器框架比较几何、概率及联合星座成形，提出含感知性能的损失函数，实现通信与感知的动态权衡，联合成形显著优于传统调制。
中文摘要 AI 辅助
6G通信系统预计将集成类似雷达的感知能力，从而实现新颖的应用场景。然而，集成感知与通信（ISAC）在通信与感知性能之间引入了权衡，因为不同任务的最优星座各不相同。本文利用自动编码器（AE）框架，对正交频分复用（OFDM）-ISAC系统中的几何、概率及联合星座成形进行了比较。我们首先推导了依赖于星座的检测概率，并提出了一种新颖的损失函数，以将感知性能纳入AE框架。仿真结果表明，星座成形能够在通信与感知之间实现动态权衡。根据优先考虑感知还是通信性能，几何或概率星座成形更为合适。联合星座成形结合了几何与概率成形的优势，显著优于传统调制格式。
英文摘要
6G communications systems are expected to integrate radar-like sensing capabilities enabling novel use cases. However, integrated sensing and communications (ISAC) introduces a trade-off between communications and sensing performance because the optimal constellations for each task differ. In this paper, we compare geometric, probabilistic and joint constellation shaping for orthogonal frequency division multiplexing (OFDM)-ISAC systems using an autoencoder (AE) framework. We first derive the constellation-dependent detection probability and propose a novel loss function to include the sensing performance in the AE framework. Our simulation results demonstrate that constellation shaping enables a dynamic trade-off between communications and sensing. Depending on whether sensing or communications performance is prioritized, geometric or probabilistic constellation shaping is preferred. Joint constellation shaping combines the advantages of geometric and probabilistic shaping, significantly outperforming legacy modulation formats.
