Learning-based content caching and sharing for wireless networks
Content caching at base stations (BSs) is a promising technique for future wireless networks
by reducing network traffic and alleviating server bottleneck. However, in practice, the …
by reducing network traffic and alleviating server bottleneck. However, in practice, the …
Application of compressive sensing techniques in distributed sensor networks: A survey
T Wimalajeewa, PK Varshney - arXiv preprint arXiv:1709.10401, 2017 - arxiv.org
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 …
Distributed multi-view sparse vector recovery
In this paper, we consider a multi-view compressed sensing problem, where each sensor
can only obtain a partial view of the global sparse vector. Here the partial view means that …
can only obtain a partial view of the global sparse vector. Here the partial view means that …
Centralized and distributed online learning for sparse time-varying optimization
SM Fosson - IEEE Transactions on Automatic Control, 2020 - ieeexplore.ieee.org
The development of online algorithms to track time-varying systems has drawn a lot of
attention in the last years, in particular in the framework of online convex optimization …
attention in the last years, in particular in the framework of online convex optimization …
Decentralized joint-sparse signal recovery: A sparse Bayesian learning approach
This work proposes a decentralized, iterative, sparse Bayesian learning algorithm for in-
network estimation of multiple joint-sparse vectors by a network of nodes, using noisy and …
network estimation of multiple joint-sparse vectors by a network of nodes, using noisy and …
Distributed recovery of jointly sparse signals under communication constraints
SM Fosson, J Matamoros… - IEEE Transactions on …, 2016 - ieeexplore.ieee.org
The problem of the distributed recovery of jointly sparse signals has attracted much attention
recently. Let us assume that the nodes of a network observe different sparse signals with …
recently. Let us assume that the nodes of a network observe different sparse signals with …
A fast sparse azimuth super-resolution imaging method of real aperture radar based on iterative reweighted least squares with linear sketching
X Tuo, Y Zhang, Y Huang, J Yang - IEEE Journal of Selected …, 2021 - ieeexplore.ieee.org
It is greatly significant to achieve radar forward-looking region imaging. Due to the limitation
of phase ambiguity and small Doppler gradient in forward-looking region, synthetic aperture …
of phase ambiguity and small Doppler gradient in forward-looking region, synthetic aperture …
Generic cooperative and distributed algorithm for recovery of signals with the same sparsity profile in wireless sensor networks: a non-convex approach
Most of the wireless sensor networks (WSNs) are equipped with battery-powered devices
with limited processing/communication resources which necessitate the designed …
with limited processing/communication resources which necessitate the designed …
[PDF][PDF] Compressive sensing based signal processing in wireless sensor networks: A survey
T Wimalajeewa, PK Varshney - arXiv preprint arXiv:1709.10401, 2017 - researchgate.net
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 …
[图书][B] Compressed sensing for distributed systems
Compressed sensing is a new technique for nonadaptive compressed acquisition, which
takes advantage of signal sparsity and allows signal recovery starting from few linear …
takes advantage of signal sparsity and allows signal recovery starting from few linear …