Deep Learning Sparse Array Design Using Binary Switching Configurations

Syed A. Hamza; Kyle Juretus; Moeness G. Amin; Fauzia Ahmad

ICASSP 2023accepted0 citations

Deep Learning Sparse Array Design Using Binary Switching Configurations

Syed A. Hamza, Kyle Juretus, Moeness G. Amin, Fauzia Ahmad

Abstract

Deep learning has been shown to be a powerful tool in array processing. Sparse array reconfigurability can be an integral part of cognitive sensing in dynamic radio frequency (RF) environments. In this respect, fast-switching that avoids hardware complexity, insertion loss, and crosstalk distortion is paramount to a realizable perception-action cycle. In this paper, we design sparse arrays using binary switching per RF chain for optimum beamforming that maximizes signal-to-interference-and-noise ratio (SINR). We apply two binary switching strategies and examine their achievable sparse array classification rates and SINR using convolutional neural networks as well as the less complex structure of multilayer perceptron.

BibTeX

@inproceedings{icassp2023_deeplearningspar,
  title = {Deep Learning Sparse Array Design Using Binary Switching Configurations},
  author = {Syed A. Hamza and Kyle Juretus and Moeness G. Amin and Fauzia Ahmad},
  booktitle = {ICASSP 2023},
  year = {2023}
}