Can I read Beurling-Selberg Extremization for Dual-Blind Deconvolution Recovery in Joint Radar-Communications on EtoBox?

Beurling-Selberg Extremization for Dual-Blind Deconvolution Recovery in Joint Radar-Communications by Monsalve, Jonathan; Vargas, Edwin; Mishra, Kumar Vijay; Sadler, Brian M.; Arguello, Henry is a scholarly article available to read on EtoBox.

What is Beurling-Selberg Extremization for Dual-Blind Deconvolution Recovery in Joint Radar-Communications about?

Recent interest in integrated sensing and communications has led to the design of novel signal processing techniques to recover information from an overlaid radar-communications signal. Here, we focus on a spectral coexistence scenario, wherein the channels and transmit signals of both radar and communications systems are unknown to the common receiver. In this dual-blind deconvolution (DBD) problem, the receiver admits a multi-carrier wireless communications signal that is overlaid with the radar signal reflected off multiple targets. The communications and radar channels are represented by continuous-valued range-times or delays corresponding to multiple transmission paths and targets, respectively. Prior works addressed recovery of unknown channels and signals in this ill-posed DBD problem through atomic norm minimization but contingent on individual minimum separation conditions for radar and communications channels. In this paper, we provide an optimal joint separation condition using extremal functions from the Beurling-Selberg interpolation theory. Thereafter, we formulate DBD as a low-rank modified Hankel matrix retrieval and solve it via nuclear norm minimization. We estim

Author

Monsalve, Jonathan; Vargas, Edwin; Mishra, Kumar Vijay; Sadler, Brian M.; Arguello, Henry

Published

2022

Language

EN