AI 中文总结
针对SEFDM用于ISAC时的S-ICI与D-ICI干扰问题，提出IMNet及IMNet-LR框架，实现了高精度的距离与速度估计，且执行时间远低于传统方法。
AI 中文摘要
频谱高效频分复用（SEFDM）是一种极具吸引力的波形，可通过压缩子载波间隔提升通信频谱效率，但将其应用于集成感知与通信（ISAC）时会面临根本性的感知挑战。具体而言，子载波正交性的人为缺失会产生SEFDM诱导的载波间干扰（S-ICI），该干扰与运动目标产生的多普勒诱导载波间干扰（D-ICI）叠加，会模糊距离-速度图并严重降低感知精度。本文基于多用户多输入多输出（MIMO）SEFDM系统，开发了一种模型驱动的ISAC框架，该框架在支持频谱高效多用户通信的同时，可抑制感知过程中的S-ICI与D-ICI。为此，本文提出了载波间干扰抑制网络（IMNet），该网络利用两种干扰的不同物理结构：一组多普勒校正滤波器先在多个多普勒假设上补偿与速度相关的D-ICI，随后轴向注意力网络抑制残余D-ICI与长距离S-ICI，以恢复可靠的感知信号。为进一步提升距离与速度估计精度，本文提出了带局部细化的IMNet（IMNet-LR），该方法在IMNet检测结果周围利用干扰项投影执行最大似然细化，无需穷举全局搜索即可实现亚单元精度。仿真结果表明，与传统检测方法相比，IMNet-LR实现了接近最大似然的距离与速度估计精度，同时执行时间降低了三个数量级以上。
英文摘要
Spectrally efficient frequency-division multiplexing (SEFDM) is an attractive waveform to improve communication spectral efficiency by compressing the subcarrier spacing, yet its use for integrated sensing and communication (ISAC) poses a fundamental sensing challenge. Specifically, the intentional loss of subcarrier orthogonality generates SEFDM-induced intercarrier interference (S-ICI), which combines with Doppler-induced ICI (D-ICI) from moving targets to blur range--velocity maps and severely degrade sensing accuracy. Building on multi-user multi-input-multi-output (MIMO) SEFDM systems, this paper develops a model-driven ISAC framework that supports spectrally efficient multi-user communication while mitigating both S-ICI and D-ICI in sensing. To this end, an intercarrier interference mitigation network (IMNet) is proposed, which exploits the distinct physical structures of the two interferences. A bank of Doppler correction filters first compensates the velocity-dependent D-ICI over multiple Doppler hypotheses, and an axial-attention network subsequently suppresses the residual D-ICI and the long-range S-ICI to recover reliable sensing signals. To further improve range and velocity estimation accuracy, IMNet with local refinement (IMNet-LR) is proposed, which performs maximum-likelihood refinement with nuisance projection around the IMNet detections to achieve sub-cell precision without an exhaustive global search. Simulation results show that IMNet-LR achieves near-maximum-likelihood range and velocity estimation accuracy with more than three orders of magnitude lower execution time compared to conventional detection methods.
