Constellation-Independent Range Estimation in Payload-Based OFDM-ISAC
Dongil Yang, Kaitao Meng, Christos Masouros, Kawon Han

TL;DR
This paper introduces a novel region-of-interest mismatched filter for payload-based OFDM-ISAC that suppresses sidelobes, maintains target ranging accuracy, and is efficiently implementable, validated through simulations and real-world tests.
Contribution
It proposes a new ROI-MMF design for OFDM-ISAC that improves sidelobe suppression and ranging accuracy while reducing computational complexity, with theoretical analysis and experimental validation.
Findings
The ROI-MMF achieves near-CRB ranging MSE.
The design maintains performance with non-constant-modulus constellations.
Experimental results confirm the effectiveness of the proposed method.
Abstract
Orthogonal frequency division multiplexing (OFDM) is a key waveform for integrated sensing and communication (ISAC) due to its spectral efficiency and compatibility with modern wireless standards. In multi-target and clutter-rich environments, however, payload-based OFDM-ISAC can suffer from data-dependent sidelobes induced by non-constant-modulus modulation symbols. To overcome these limitations, this paper proposes a region-of-interest mismatched filter (ROI-MMF) that suppresses sidelobes within a prescribed delay region while preserving the mainlobe response. By leveraging the Woodbury identity, the proposed design admits an efficient closed-form implementation whose complexity scales with the ROI size rather than the number of subcarriers. We theoretically provide the ranging mean-square error (MSE) of the designed ROI-MMF, which shows the superior performance compared to…
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