Tongxin xuebao, 2017 In multi-hypothesis based distributed compressed video sensing systems,the quality of the multi-hypothesis set has important influence on the reconstruction performance of decoder.However,the acquiring of the hypothesis set has not been concerned in ...
Yong-hong KUO, Ru-quan WANG, Jian CHEN
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Statistical mechanics analysis of thresholding 1-bit compressed sensing
, 2016 The one-bit compressed sensing framework aims to reconstruct a sparse signal by only using the sign information of its linear measurements. To compensate for the loss of scale information, past studies in the area have proposed recovering the signal by ...
Kabashima, Yoshiyuki, Xu, Yingying
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Sparse Recovery Optimization in Wireless Sensor Networks with a Sub-Nyquist Sampling Rate
Sensors, 2015 Compressive sensing (CS) is a new technology in digital signal processing capable of high-resolution capture of physical signals from few measurements, which promises impressive improvements in the field of wireless sensor networks (WSNs).
Davide Brunelli, Carlo Caione
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A Sparsity Adaptive Algorithm for Wideband Compressive Spectrum Sensing
Dianxin kexue, 2014 Traditional spectrum sensing based on compressed sensing assumes that the sparsity is known, in fact,it is unknown and time-varying. To solve the problem, a sparsity adaptive algorithm for wideband spectrum sensing was proposed.
Zhijin Zhao, Junwei Hu
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Probability-based pilot allocation for MIMO relay Distributed compressed sensing channel estimation
EURASIP Journal on Advances in Signal Processing, 2018 Multiple-Input Multiple-Output (MIMO) relay communication systems are used as an efficient system in spectral efficiency and power allocation view point. In these systems, some of the facilities need channel state information (CSI).
Abbas Akbarpour-Kasgari +1 more
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Distributed Compressed Sensing Aided Sparse Channel Estimation in FDD Massive MIMO System
IEEE Access, 2018 Massive multi-input multi-output (MIMO), which employs large number of antennas at the base station, can significantly boost the spectral efficiency and multiplexing gain.
Ruoyu Zhang, Honglin Zhao, Jiayan Zhang
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1 bit Compressive Spectrum Sensing Algoritbm Based on Distributed Model
Dianxin kexue, 2014 Since the actual sparsity of spectrum is unknown and time-varying, information transmit frequently between nodes in the distributed spectrum sensing network consumes communication bandwidth.
Zhijin Zhao, Weikang Hu, Junwei Hu
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Distributed compressed sensing for the MIMO MAC with correlated sources [PDF]
, 2012 Steven Corroy, Rudolf Mathar
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Distributed and Cooperative Compressive Sensing Recovery Algorithm for Wireless Sensor Networks with Bi-directional Incremental Topology [PDF]
, 2021 Ghanbar Azarnia +2 more
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Distributed Compressive Sensing: Performance Analysis with Diverse Signal Ensembles [PDF]
, 2018 Sung-Hsien Hsieh +3 more
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