Circulant ADMM-Net for Fast High-resolution DoA Estimation

Code

• github.com/youvalklioui/cadmmnet
  Officialpytorch★ 9

Abstract

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(N(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 N^2 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.

Tasks

Reproductions