We study centralized interpolation of bandlimited graph signals at a fusion center (FC), when sampled data are transmitted over rate-constrained links. In such a scenario, the performance of the reconstruction task is inevitably affected by several sources of errors such as observation noise and quantization due to source encoding. In this paper, we propose two strategies for optimally selecting transmission powers, quantization bits, and the sampling set, with the aim of interpolating a graph signal with guaranteed performance. Numerical results validate the proposed approach for interpolation of bandlimited graph signals under communication constraints
With the explosive growth of information and communication, data is being generated at an unpreceden...
Distributed graph signal processing algorithms require the network nodes to communicate by exchangin...
The rapid development of signal processing on graphs provides a new perspective for processing large...
We study centralized interpolation of bandlimited graph signals at a fusion center (FC), when sample...
New schemes to recover signals defined in the nodes of a graph are proposed. Our focus is on reconst...
International audienceWe study the problem of sampling k-bandlimited signals on graphs. We propose t...
International audienceWe consider the problem of signal interpolation on graphs, i.e. recovering one...
Continuous-time signals are well known for not being perfectly localized in both time and frequency ...
In applications such as social, energy, transportation, sensor, and neuronal networks, big data natu...
Multiscale analysis of signals on graphs often involves the downsampling of a graph. In this paper, ...
Recovery of a graph signal from samples has many important applications in signal processing over ne...
In this paper the focus is on sampling and reconstruction of signals supported on nodes of arbitrary...
In this paper, we present two localized graph filtering based meth-ods for interpolating graph signa...
The aim of this paper is to propose a least mean squares (LMS) strategy for adaptive estimation of s...
Compressed Sensing teaches us that measurements can be traded for offline computation if the signal ...
With the explosive growth of information and communication, data is being generated at an unpreceden...
Distributed graph signal processing algorithms require the network nodes to communicate by exchangin...
The rapid development of signal processing on graphs provides a new perspective for processing large...
We study centralized interpolation of bandlimited graph signals at a fusion center (FC), when sample...
New schemes to recover signals defined in the nodes of a graph are proposed. Our focus is on reconst...
International audienceWe study the problem of sampling k-bandlimited signals on graphs. We propose t...
International audienceWe consider the problem of signal interpolation on graphs, i.e. recovering one...
Continuous-time signals are well known for not being perfectly localized in both time and frequency ...
In applications such as social, energy, transportation, sensor, and neuronal networks, big data natu...
Multiscale analysis of signals on graphs often involves the downsampling of a graph. In this paper, ...
Recovery of a graph signal from samples has many important applications in signal processing over ne...
In this paper the focus is on sampling and reconstruction of signals supported on nodes of arbitrary...
In this paper, we present two localized graph filtering based meth-ods for interpolating graph signa...
The aim of this paper is to propose a least mean squares (LMS) strategy for adaptive estimation of s...
Compressed Sensing teaches us that measurements can be traded for offline computation if the signal ...
With the explosive growth of information and communication, data is being generated at an unpreceden...
Distributed graph signal processing algorithms require the network nodes to communicate by exchangin...
The rapid development of signal processing on graphs provides a new perspective for processing large...