  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
$\ell_1$-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 experiments ADMM-Net exhibits markedly lower error and computational
cost than ADMM and CVX.
