Search papers, labs, and topics across Lattice.
This paper introduces a learning-based coarse-to-fine sensing pipeline that utilizes candidate-latent feedback for efficient single-target estimation in distributed OFDM-ISAC networks. By employing a lightweight convolutional scorer and a learned encoder, the method generates compact candidate tokens that significantly reduce the data transmitted between the sensing entity and the sensing function, achieving high detection rates with drastically lower bandwidth requirements. The results demonstrate that the proposed approach maintains robust performance across different environments, achieving up to 99.50% detection accuracy while compressing data transmission from multi-Gbit/s to sub-Mbit/s rates.
Achieving up to 99.50% detection accuracy while slashing data transmission rates from multi-Gbit/s to sub-Mbit/s could revolutionize the efficiency of integrated sensing and communication systems.
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.
