Intelligent reflecting surface (IRS) is a rapidly emerging paradigm to enable non-line-of-sight (NLoS) wireless transmission.
The mutual interference between similar radar systems can result in reduced radar sensitivity and increased false alarm rates.
Similar to the case of the arcsine law, the Bussgang law only considers a zero sampling threshold.
The proposed deep architecture is able to learn an alternative sensing matrix by taking advantage of the underlying unfolded algorithm such that the resulting learned recovery algorithm can accurately and quickly (in terms of the number of iterations) recover the underlying compressed signal of interest from its one-bit noisy measurements.
Phase retrieval in optical imaging refers to the recovery of a complex signal from phaseless data acquired in the form of its diffraction patterns.
We demonstrate that the IRS can provide a virtual or non-line-of-sight (NLOS) link between the radar and target leading to an enhanced radar performance.
The need to recover high-dimensional signals from their noisy low-resolution quantized measurements is widely encountered in communications and sensing.
Specifically, we first investigate the identifiability conditions for the DoA estimation problem from one-bit SLA data and establish an equivalency with the case when DoAs are estimated from infinite-bit unquantized measurements.
Exploring the idea of phase retrieval has been intriguing researchers for decades, due to its appearance in a wide range of applications.
The problem of phase retrieval has been intriguing researchers for decades due to its appearance in a wide range of applications.
In this paper, we investigate the joint optimization of the waveform covariance matrix and the antenna position vector for a MIMO radar system to approximate a given transmit beam-pattern, as well as to minimize the cross-correlation between the probing signals at a number of given target locations.
The lack of interpretability in current deep learning models causes serious concerns as they are extensively used for various life-critical applications.
In radar systems, unimodular (or constant-modulus) waveform design plays an important role in achieving better clutter/interference rejection, as well as a more accurate estimation of the target parameters.
Parameterized mathematical models play a central role in understanding and design of complex information systems.
The task of a personalization system is to recommend items or a set of items according to the users' taste, and thus predicting their future needs.
Machine learning, and more specifically deep learning, have shown remarkable performance in sensing, communications, and inference.