2020 2nd 6G Wireless Summit (6G SUMMIT) 2020
DOI: 10.1109/6gsummit49458.2020.9083783
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Quantized Compressed Sensing via Deep Neural Networks

Abstract: Compressed sensing (CS) is an efficient technique to acquire sparse signals in many wireless applications to, e.g., reduce the amount of data and save low-power sensors' batteries. This paper addresses efficient acquisition of sparse sources through quantized noisy compressive measurements where the encoder and decoder are realized by deep neural networks (DNNs). We devise a DNN based quantized compressed sensing (QCS) method aiming at minimizing the mean-square error of the signal reconstruction. Once trained… Show more

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Cited by 5 publications
(3 citation statements)
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“…Hence, compressive sensing is also a way to reduce the amount of data that need to be exchanged for required communication [149]. Compressive sensing is already known from sensor networks [152], [153]. Fundamental concepts and widely used algorithms for compressive sensing are comprised in [154].…”
Section: B Energy Reduction Potential Beyond 5gmentioning
confidence: 99%
“…Hence, compressive sensing is also a way to reduce the amount of data that need to be exchanged for required communication [149]. Compressive sensing is already known from sensor networks [152], [153]. Fundamental concepts and widely used algorithms for compressive sensing are comprised in [154].…”
Section: B Energy Reduction Potential Beyond 5gmentioning
confidence: 99%
“…In C-TISTA, they use a trainable shrinkage function to utilize various prior information such as sparsity. While Mahabadi et al try to learn the sampling process of the quantized CS [261], Leinonen and Codreanu directly jointly optimize the whole sampling and recovery process with an encoder and decoder via NNs [262]. A similar method for joint optimization of measurement and recovery in can quantized CS also be found in [263], where the NN consists a binary measurement matrix, a non-uniform quantizer, and a non-iterative recovery solver.…”
Section: Solving Nonlinear Inverse Problems With DLmentioning
confidence: 99%
“…There are few papers have examined the effect of quantization on CS using deep networks. For example, Leinonen and Codreanu (2020b) used a fully connected network with quantization to perform quantized CS. In another study, a neural network is presented for quantized CS of wireless neural recording (Sun et al, 2016).…”
mentioning
confidence: 99%