发表机构
The University of Sydney; Technische Universität Berlin; La Trobe University; Data 61, CSIRO（悉尼大学; 柏林工业大学; 拉筹伯大学; CSIRO数据61）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种基于深度学习的编码波形设计方法，用于MIMO-OFDM架构下的ISAC系统，通过Transformer编码器联合优化误码率与测距精度，实现通信与感知性能的灵活权衡。
AI 中文摘要
本文提出了一种基于深度学习（DL）的编码波形设计，用于集成感知与通信（ISAC），在短块传输中实现通信可靠性与测距精度之间的灵活权衡。所提出的方案建立在实用的多输入多输出正交频分复用（MIMO-OFDM）架构之上，其中假设发射机已知通信信道状态信息和静态目标的角度。基于Transformer的发射机将输入信息比特直接编码为ISAC发射波形，以联合优化误码率（BER）性能和延迟修正的Cramér-Rao界（MCRB）。在通信侧采用相应的基于Transformer的接收机来恢复传输的信息比特。我们进一步检查了面向通信和面向感知设计的学习码字，揭示出均衡的ISAC波形自然呈现出介于这两种极端情况之间的中间结构。数值结果展示了这些码字结构，并证明所提出的设计相比基于标准信道编码和调制的传统方案提供了显著的权衡增益。
英文摘要
This paper proposes a deep learning (DL)-based coded waveform design for integrated sensing and communications (ISAC), enabling flexible trade-offs between communication reliability and ranging accuracy in short-block transmissions. The proposed scheme is built upon a practical multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) architecture, where the communication channel state information and the angles of the static targets are assumed available at the transmitter. A transformer-based transmitter encodes input information bits directly into ISAC transmit waveforms to jointly optimize the bit error rate (BER) performance and the delay modified Cramer-Rao bound (MCRB). A corresponding transformer-based receiver is adopted at the communication side to recover the transmitted information bits. We further examine the learned codewords for communication-oriented and sensing-oriented designs, revealing that a balanced ISAC waveform naturally exhibits an intermediate structure between these two extremes. Numerical results illustrate these codeword structures and demonstrate that the proposed design provides substantial trade-off gains over conventional schemes based on standard channel coding and modulation.
