ADMM-Net for Communication Interference Removal in Stepped-Frequency Radar
Jeremy Johnston, Yinchuan Li, Marco Lops, Xiaodong Wang

TL;DR
ADMM-Net is a novel neural network architecture inspired by ADMM, designed to effectively remove communication interference and enhance super-resolution radar imaging in spectrum-sharing scenarios.
Contribution
It introduces a complex-valued neural network based on ADMM iterations with learnable parameters for interference removal in radar imaging, tailored for spectrum-sharing environments.
Findings
Lower error compared to traditional ADMM
Reduced computational cost
Effective interference removal in super-resolution radar imaging
Abstract
Complex ADMM-Net, a complex-valued neural network architecture inspired by the alternating direction method of multipliers (ADMM), is designed for interference removal in super-resolution stepped frequency radar angle-range-doppler imaging. Tailored to an uncooperative scenario wherein a MIMO radar shares spectrum with communications, the ADMM-Net recovers the radar image---which is assumed to be sparse---and simultaneously removes the communication interference, which is modeled as sparse in the frequency domain owing to spectrum underutilization. The scenario motivates an -minimization problem whose ADMM iteration, in turn, undergirds the neural network design, yielding a set of generalized ADMM iterations that have learnable hyperparameters and operations. To train the network we use random data generated according to the radar and communication signal models. In numerical…
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Taxonomy
MethodsAlternating Direction Method of Multipliers
