Bridging the Gap via Data-Aided Sensing: Can Bistatic ISAC Converge to Genie Performance?

TL;DR

This paper proposes a multi-stage data-aided iterative sensing method for bistatic OFDM ISAC systems, significantly improving target detection and narrowing the performance gap with idealized genie-aided systems.

Contribution

It introduces a novel multi-stage bistatic OFDM receiver that iteratively refines sensing and data estimates, enhancing detection performance beyond pilot-only methods.

Findings

Outperforms pilot-only sensing in multi-target scenarios

Narrowing the gap to genie-aided system performance

Expands the ISAC trade-off region close to theoretical limits

Abstract

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.