Learning Based Reconfigurable Sub-nyquist Sampling Framework for Ultra-wideband Angular Sensing
Himani Joshi, Mohammad Alaee-Kerahroodi, Achanna Anil Kumar, R. Bhavani Shankar Mysore, Sumit Jagdish Darak
Abstract
In this work, an intelligent and reconfigurable ultra-wideband angular sensing (UWAS) framework is proposed which is independent of the maximum number of active transmissions in a wideband spectrum unlike the existing UWAS methods. To perform the above task, we propose a sub-Nyquist sampling and sparse ruler based multi-antenna array receiver architecture. By characterizing and selecting a set of frequency bands via a learning algorithm, the proposed receiver allows sensing of more active transmissions than the number of antenna over an unlimited bandwidth. The simulation results show that due to the learning based approach, the proposed UWAS outperforms when compared to non-learning based UWAS method.
BibTeX
@inproceedings{icassp2020_learningbasedrec,
title = {Learning Based Reconfigurable Sub-nyquist Sampling Framework for Ultra-wideband Angular Sensing},
author = {Himani Joshi and Mohammad Alaee-Kerahroodi and Achanna Anil Kumar and R. Bhavani Shankar Mysore and Sumit Jagdish Darak},
booktitle = {ICASSP 2020},
year = {2020}
}