Quantized compressed sensing via deep neural networks
Leinonen, Markus; Codreanu, Marian (2020-05-04)
M. Leinonen and M. Codreanu, "Quantized Compressed Sensing via Deep Neural Networks," 2020 2nd 6G Wireless Summit (6G SUMMIT), Levi, Finland, 2020, pp. 1-5, doi: 10.1109/6GSUMMIT49458.2020.9083783
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https://urn.fi/URN:NBN:fi-fe2020050725529
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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 offline, the proposed method enjoys extremely fast and low complexity decoding in the online communication phase. Simulation results demonstrate the superior rate-distortion performance of the proposed method compared to a polynomial-complexity QCS reconstruction scheme.
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