Papers › Circulant ADMM-Net for Fast High-resolution DoA Estimation
Circulant ADMM-Net for Fast High-resolution DoA Estimation
Youval Klioui
This paper introduces CADMM-Net and CHADMM-Net, two deep neural networks for direction of arrival estimation within the least-absolute shrinkage and selection operator (LASSO) framework. These two networks are based on a structured deep unfolding of the alternating direction method of multipliers (ADMM) algorithm through the use of circulant as well as Hermitian-circulant matrices. Along with a computational complexity of 𝒪(Nlog(N)) per layer for the inference, where N is the length of the dictionary 𝐀, they additionally exhibit a memory footprint of N and approximately half of N for CADMMNet and CHADMM-Net, respectively, compared with N² for ADMM-Net. Furthermore, these structured networks exhibit a competitive performance against ADMM-Net, LISTA, TLISTA, and THLISTA with respect to the detection rate, the angular root-mean square error, and the normalized mean squared error.
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