Wideband Near-Field Sensing in ISAC: Unified Algorithm Design and Decoupled Effect Analysis
Ruiyun Zhang, Zhaolin Wang, Zhiqing Wei, Yuanwei Liu, Zehui Xiong, Zhiyong Feng

TL;DR
This paper introduces a low-complexity compressed sensing algorithm for wideband near-field target localization in 6G XL-MIMO networks, effectively decoupling effects and reducing computational load.
Contribution
It proposes a novel decoupling framework and angle-distance sampling grid that significantly reduces dictionary size and computational complexity in WB-NF sensing.
Findings
The algorithm achieves robust localization performance across wideband and near-field regimes.
New coherence-based metrics effectively delineate WB and NF effect boundaries.
Simulations validate the method's efficiency and the theoretical boundary guidelines.
Abstract
To advance integrated sensing and communications (ISAC) in sixth-generation (6G) extremely large-scale multiple-input multiple-output (XL-MIMO) networks, a low-complexity compressed sensing (CS)-based dictionary design is proposed for wideband near-field (WB-NF) target localization. Currently, the massive signal dimensions in the WB-NF regime impose severe computational burdens and high spatial-frequency coherence on conventional grid-based algorithms. Furthermore, a unified framework exploiting both wideband (WB) and near-field (NF) effects is lacking, and the analytical conditions for simplifying this model into decoupled approximations remain uncharacterized. To address these challenges, the proposed algorithm mathematically decouples the mutual coherence function and introduces a novel angle-distance sampling grid with customized distance adjustments, drastically reducing dictionary…
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