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Communication-efficient federated learning based on compressed sensing
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 …
constrained environment of the Internet of Things (IoT), where multiple IoT clients train a …
Application of compressive sensing techniques in distributed sensor networks: A survey
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 …
based solutions in wireless sensor networks (WSNs) including the main ongoing/recent …
Deepfpc: A deep unfolded network for sparse signal recovery from 1-bit measurements with application to doa estimation
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 …
its application to tackling the problem of direction-of-arrival (DOA) estimation. DeepFPC is …
[PDF][PDF] Compressive sensing based signal processing in wireless sensor networks: A survey
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 …
including compressive imaging, video processing, communication, and radar to name a few …
[HTML][HTML] Research on data fusion scheme for wireless sensor networks with combined improved LEACH and compressed sensing
Y Song, Z Liu, X He, H Jiang - Sensors, 2019 - mdpi.com
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 …
data are processed and transmitted, the node energy consumption will be too fast and will …
Robust recovery in 1-bit compressive sensing via ℓq-constrained least squares
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 …
signals with sparsity level s from m noisy and sign flipped 1-bit quantized measurements …
One-bit Compressed Sensing using Generative Models
This paper addresses the classical problem of one-bit compressed sensing using a deep
learning-based reconstruction algorithm that leverages a trained generative model to …
learning-based reconstruction algorithm that leverages a trained generative model to …
A fast algorithm for joint sparse signal recovery in 1-bit compressed sensing
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 …
from multiple measurement vectors (MMV). The proposed algorithm is based on the …
Distributed collaborative spectrum sensing using 1-bit compressive sensing in cognitive radio networks
S Yan, M Liu, J Si - IEICE Transactions on Fundamentals of …, 2020 - search.ieice.org
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 …
spectrum sharing. However, the problem becomes quite challenging in wideband spectrum …
Noisy one-bit compressed sensing with side-information
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 …
measurements when the receiver has access to side-information (SI). We assume that …