We investigate data-aided iterative sensing in bistatic OFDM ISAC systems, focusing on scenarios with co-located sensing and communication receivers. To enhance target detection beyond pilot-only sensing methods, we propose a multi-stage bistatic OFDM receiver, performing iterative sensing and data demodulation to progressively refine ISAC channel and data estimates. Simulation results demonstrate that the proposed data-aided scheme significantly outperforms pilot-only benchmarks, particularly in multi-target scenarios, substantially narrowing the performance gap compared to a genie-aided system with perfect data knowledge. Moreover, the proposed approach considerably expands the bistatic ISAC trade-off region, closely approaching the probability of detection-achievable rate boundary established by its genie-aided counterpart.
@article{arxiv.2505.01280,
title = {Bridging the Gap via Data-Aided Sensing: Can Bistatic ISAC Converge to Genie Performance?},
author = {Musa Furkan Keskin and Silvia Mura and Marouan Mizmizi and Dario Tagliaferri and Henk Wymeersch},
journal= {arXiv preprint arXiv:2505.01280},
year = {2025}
}