Quantized Compressed Sensing via Deep Neural Networks

Leinonen, Markus; Codreanu, Marian

doi:10.1109/6gsummit49458.2020.9083783

Cited by 5 publications

(3 citation statements)

References 25 publications

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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%

Toward Greener 5G and Beyond Radio Access Networks—A Survey

Larsen

Christiansen

Ruepp

et al. 2023

IEEE Open J. Commun. Soc.

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Mobile network traffic is increasing and so is the energy consumption. The Radio Access Network (RAN) part is responsible for the largest share of the mobile network energy consumption, and thus; an important consideration when expanding mobile networks to meet traffic demands. This work analyses how the energy consumption of future mobile networks can be minimised by using the right RAN architecture, share the network with other operators and implementing the most efficient energy minimising technologies in the RAN. It is explored how the different approaches can be realised in real life networks as well as the research state of the art is highlighted. Furthermore, this work provides an overview of future research directions for 6G energy saving potentials. Different energy saving contributions are evaluated by a common methodology for more realistic comparison, based on the potential energy saving of the overall mobile network consumption. Results show that implementing selected technologies and architectures, the mobile network overall energy consumption can be reduced by approximately 30%, corresponding to almost half of the RAN energy consumption. Following this, a set of guidelines towards an energy optimised mobile network is provided, proposing changes to be made initially and in the longer run for brownfield network operators as well as a target network for greenfield network operators.

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Section: B Energy Reduction Potential Beyond 5gmentioning

confidence: 99%

Toward Greener 5G and Beyond Radio Access Networks—A Survey

Larsen

Christiansen

Ruepp

et al. 2023

IEEE Open J. Commun. Soc.

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show abstract

“…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%

Deep Learning Methods for Solving Linear Inverse Problems: Research Directions and Paradigms

Bai,

Chen,

Chen

et al. 2020

Preprint

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The linear inverse problem is fundamental to the development of various scientific areas. Innumerable attempts have been carried out to solve different variants of the linear inverse problem in different applications. Nowadays, the rapid development of deep learning provides a fresh perspective for solving the linear inverse problem, which has various well-designed network architectures results in state-of-the-art performance in many applications. In this paper, we present a comprehensive survey of the recent progress in the development of deep learning for solving various linear inverse problems. We review how deep learning methods are used in solving different linear inverse problems, and explore the structured neural network architectures that incorporate knowledge used in traditional methods. Furthermore, we identify open challenges and potential future directions along this research line.

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“…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).…”

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confidence: 99%

RQCSNet: A deep learning approach to quantized compressed sensing of remote sensing images

Mirrashid

Shirazi

2021

Expert Systems

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In this article, a deep neural network is proposed that can be used to perform a quantized compressed sensing of remote sensing images. This network, dubbed RQCSNet consists of three parts: sensing, nonlinear quantization, and reconstruction. All network's parts are trained simultaneously according to the input data. RQCSNet has fully convolutional architecture, and it can work with different resolutions of the input images without the need for large initial fully connected layers. Furthermore, its structure reduces the blocky artefacts in the final reconstructed images by considering the effects of different image blocks in the reconstruction process. The proposed model has the ability to learn the nonlinear quantization function according to the input data and sending the measured values from the transmitter to the receiver with a fewer bit budget. Also, RQCSNet can learn the compressed sensing reconstruction algorithm without initial image reconstruction (that is usually the first step of reconstruction in other models), which reduces network flops. The results are presented on standard datasets of UCMerced as a remote sensing data that is the main concentration of this article, and as well as the set11 which is a dataset widely used for the benchmark of the state-of-the-art models. The experiments show that RQCSNet has a better performance than existing models in terms of the quality of image reconstruction and time complexity. As a benchmark, our network has a mean PSNR improvement of about 1.2 dB in reconstructed images compared to available techniques, at a measurement rate of 0.25. The effect of adding noise to the quantized measured data from the transmitter to the receiver at the time of transmission is also investigated in this article, and it is shown that the quality of image reconstruction will be robust to the noise once adding a noise layer to the network during training.

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Quantized Compressed Sensing via Deep Neural Networks

Cited by 5 publications

References 25 publications

Toward Greener 5G and Beyond Radio Access Networks—A Survey

Toward Greener 5G and Beyond Radio Access Networks—A Survey

Deep Learning Methods for Solving Linear Inverse Problems: Research Directions and Paradigms

RQCSNet: A deep learning approach to quantized compressed sensing of remote sensing images

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