面向任务的候选隐变量反馈：分布式OFDM-ISAC网络中的粗到细感知
Task-Oriented Candidate-Latent Feedback for Coarse-to-Fine Sensing in Distributed OFDM-ISAC Networks
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- Indian Institute of Technology (IIT), Madras（印度马德拉斯理工学院）
- Centre of Excellence in Wireless Technology (CEWiT)（无线技术卓越中心）
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中文总结 AI 辅助
该研究针对分布式OFDM-ISAC网络的SE-SF接口，提出基于学习的粗到细感知流水线与候选隐变量反馈方法，实现高检测率与低传输速率，且跨场景泛化性能良好。
中文摘要 AI 辅助
未来的集成感知与通信（ISAC）架构将获取测量值的感知实体（SE）与执行推理的感知功能（SF）分离，因此需要在SE-SF接口上提供紧凑的、面向任务的反馈。转发原始信道频率响应或完整的每链路延迟-多普勒-方位角-仰角（DDAE）张量的成本过高，而仅报告峰值则会丢失杂波下的目标判别结构。我们提出一种基于学习的粗到细感知流水线，采用候选隐变量反馈用于单目标估计。在SE端，轻量卷积评分器从基于导频的OFDM信道估计中生成密集的延迟-多普勒候选图，学习到的编码器通过融合每个候选的方位角-仰角块、归一化位置和置信度线索，构建K个紧凑的C维候选令牌。这些隐变量在训练后被均匀量化为b位，并在有限预算B_fb = bKC + 18K + 16位下传输至SF，SF执行跨候选的细化、重排序和联合四参数估计。在包含静态和动态杂波的光线追踪城市场景中，（K,C,b）设计空间的三个工作点在每个相干处理间隔下实现107-806字节的数据传输时，达到96.33-98.88%的检测率，相对于8位DDAE幅度张量的压缩比为1.2-9.2×10^4，将SE-SF接口速率从多Gbit/s降至亚Mbit/s。在独立校园规模环境的跨场景评估中，无需重新训练即可实现98.79-99.50%的检测率和相当或更优的角度精度，表明学习到的表示捕获了可在杂波密度相当或更低的场景间迁移的目标相关结构。
英文摘要
Future integrated sensing and communication (ISAC) architectures separate the sensing entity (SE) that acquires measurements from the sensing function (SF) that performs inference, creating a need for compact, task-oriented feedback on the SE-SF interface. Forwarding the raw channel frequency response or full per-link delay-Doppler-azimuth-elevation (DDAE) tensor is prohibitively expensive, while peak-only reporting discards target-discriminative structure under clutter. We propose a learning-based coarse-to-fine sensing pipeline with candidate-latent feedback for single-target estimation. At the SE, a lightweight convolutional scorer produces a dense delay-Doppler proposal map from pilot-based OFDM channel estimates, and a learned encoder constructs K compact C-dimensional candidate tokens by fusing per-candidate azimuth-elevation patches, normalized position, and confidence cues. The latents are uniformly quantized post-training to b bits and transmitted under a finite budget B_fb = bKC + 18K + 16 bits to the SF, which performs cross-candidate refinement, reranking, and joint four-parameter estimation. On a ray-traced urban scene with static and dynamic clutter, three operating points in the (K, C, b) design space achieve 96.33-98.88% detection at 107-806 bytes per coherent processing interval, compression ratios of 1.2-9.2 x 10^4 over the 8-bit DDAE magnitude tensor, reducing the SE-SF interface from multi-Gbit/s to sub-Mbit/s rates. Cross-scene evaluation on an independent campus-scale environment achieves 98.79-99.50% detection and at-or-better angular accuracy without retraining, indicating that the learned representation captures target-relevant structure that transports across scenes of comparable or lower clutter density.
