In this article, we investigate the problem of federated learning (FL) in a communication-
constrained environment of the Internet of Things (IoT), where multiple IoT clients train a …

In this survey paper, our goal is to discuss recent advances of compressive sensing (CS)
based solutions in wireless sensor networks (WSNs) including the main ongoing/recent …

In this paper, we introduce a novel deep neural network, coined DeepFPC, and investigate
its application to tackling the problem of direction-of-arrival (DOA) estimation. DeepFPC is …

Compressive sensing (CS) has been shown to be promising in a wide variety of applications
including compressive imaging, video processing, communication, and radar to name a few …

There are a lot of redundant data in wireless sensor networks (WSNs). If these redundant
data are processed and transmitted, the node energy consumption will be too fast and will …

In this paper, we propose using ℓ q-constrained least-squares to decode n dimensional
signals with sparsity level s from m noisy and sign flipped 1-bit quantized measurements …

In this letter, a new algorithm is proposed for fast 1-bit compressed sensing (CS) recovery
from multiple measurement vectors (MMV). The proposed algorithm is based on the …

In cognitive radio (CR) networks, spectrum sensing is an essential task for enabling dynamic
spectrum sharing. However, the problem becomes quite challenging in wideband spectrum …

Classification is an important task in the fields of signal processing and machine learning.
Recently, compressive classification (CC) appears to enable signal classification directly …

We consider the problem of sparse signal reconstruction from noisy one-bit compressed
measurements when the receiver has access to side-information (SI). We assume that …