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- Title
ADMM-Net for Beamforming Based on Linear Rectification with the Atomic Norm Minimization.
- Authors
Gong, Zhenghui; Zhang, Xinyu; Ren, Mingjian; Su, Xiaolong; Liu, Zhen
- Abstract
Target misalignment can cause beam pointing deviations and degradation of sidelobe performance. In order to eliminate the effect of target misalignment, we formulate the jamming sub-space recovery problem as a linearly modified atomic norm-based optimization. Then, we develop a deep-unfolding network based on the alternating direction method of multipliers (ADMM), which effectively improves the applicability and efficiency of the algorithm. By using the back-propagation process of deep-unfolding networks, the proposed method could optimize the hyper-parameters in the original atomic norm. This feature enables the adaptive beamformer to adjust its weight according to the observed data. Specifically, the proposed method could determine the optimal hyper-parameters under different interference noise matrix conditions. Simulation results demonstrate that the proposed network could reduce computational cost and achieve near-optimal performance with low complexity.
- Subjects
BEAMFORMING; NOISE; ALGORITHMS; RADAR interference
- Publication
Remote Sensing, 2024, Vol 16, Issue 1, p96
- ISSN
2072-4292
- Publication type
Article
- DOI
10.3390/rs16010096