Deep Learning-Based Compressive Sampling Optimization in Massive MIMO Systems

Saidur R. Pavel; Yimin D. Zhang; Maria S. Greco; Fulvio Gini

ICASSP 2023accepted0 citations

Deep Learning-Based Compressive Sampling Optimization in Massive MIMO Systems

Saidur R. Pavel, Yimin D. Zhang, Maria S. Greco, Fulvio Gini

Abstract

In this paper, we develop a deep learning framework to optimize the compressive sampling matrix in a massive multiple-input multiple-output (MIMO) system. The optimized compressive sampling matrix is utilized to project high-dimensional data received at the massive MIMO system into a lower-dimensional space so that the directions of arrival and other signal parameters can be efficiently obtained with a reduced hardware complexity. The proposed deep learning approach for optimizing the compressive measurement matrix increases its robustness and generalizability.

BibTeX

@inproceedings{icassp2023_deeplearningbase,
  title = {Deep Learning-Based Compressive Sampling Optimization in Massive MIMO Systems},
  author = {Saidur R. Pavel and Yimin D. Zhang and Maria S. Greco and Fulvio Gini},
  booktitle = {ICASSP 2023},
  year = {2023}
}