用于集成感知与通信的短码长设计：一种深度学习方法
Short-Length Code Designs for Integrated Sensing and Communications: A Deep Learning Approach
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中文总结 AI 辅助
本文提出基于自编码器的深度学习框架，用于非相干ISAC短码长波形设计，通过修正Cramér-Rao界和最大似然解码分析揭示通信与感知的折衷，并联合优化两者，仿真证明在短块长度和衰落条件下优于传统方案。
中文摘要 AI 辅助
集成感知与通信（ISAC）利用共享波形实现通信与感知的联合功能，但其信号设计因两个目标之间的固有折衷而具有挑战性，尤其是在短块长度体制下。本文提出了一种基于自编码器（AE）的框架，用于非相干环境下的ISAC波形设计。我们推导了用于多目标时延估计的修正Cramér-Rao界，并分析了相关衰落条件下非相干通信的最大似然解码规则。这些结果揭示了波形设计中通信与感知目标之间的结构联系和折衷。基于此分析，自编码器学习能够联合优化两种功能的波形表示，并通过一个可调参数控制折衷。仿真结果表明，所提出的设计在通信可靠性和感知精度方面均优于传统方案，尤其是在短块长度和衰落条件下。
英文摘要
Integrated sensing and communication (ISAC) enables joint communication and sensing using a shared waveform, but its signal design is challenging due to the inherent trade-off between the two objectives, particularly in the short blocklength regime. This paper proposes an autoencoder (AE)-based framework for ISAC waveform design in noncoherent settings. We derive a modified Cramér-Rao bound for multi-target delay estimation and analyze the maximum-likelihood decoding rule for noncoherent communication under correlated fading. These results reveal structural connections and trade-offs between communication and sensing objectives in waveform design. Based on this analysis, the AE learns waveform representations that jointly optimize both functionalities, with a tunable parameter controlling the trade-off. Simulation results show that the proposed design outperforms conventional schemes in both communication reliability and sensing accuracy, especially under short blocklength and fading conditions.
