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Adaptive Array Signal Processing

Spatial Array Thinning for Interference Cancellation under Connectivity Constraints


Array spatial thinning is employed to select the most effective antenna elements in a large phased array for optimum performance concerning hardware and computational costs, in conjunction with managing element failure and radio interference mitigation. We formulate spatial array thinning under connectivity constraints to make the thinning applicable in large arrays. By introducing graph optimization, the problem is recast as a k-clique version of a generalized minimum clique problem.

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Authors:
Hamed Nosrati, Elias Aboutanios, David B. Smith
Submitted On:
12 April 2018 - 10:26pm
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ICASSP2018_A0.pdf

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[1] Hamed Nosrati, Elias Aboutanios, David B. Smith, "Spatial Array Thinning for Interference Cancellation under Connectivity Constraints", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2558. Accessed: May. 30, 2020.
@article{2558-18,
url = {http://sigport.org/2558},
author = {Hamed Nosrati; Elias Aboutanios; David B. Smith },
publisher = {IEEE SigPort},
title = {Spatial Array Thinning for Interference Cancellation under Connectivity Constraints},
year = {2018} }
TY - EJOUR
T1 - Spatial Array Thinning for Interference Cancellation under Connectivity Constraints
AU - Hamed Nosrati; Elias Aboutanios; David B. Smith
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2558
ER -
Hamed Nosrati, Elias Aboutanios, David B. Smith. (2018). Spatial Array Thinning for Interference Cancellation under Connectivity Constraints. IEEE SigPort. http://sigport.org/2558
Hamed Nosrati, Elias Aboutanios, David B. Smith, 2018. Spatial Array Thinning for Interference Cancellation under Connectivity Constraints. Available at: http://sigport.org/2558.
Hamed Nosrati, Elias Aboutanios, David B. Smith. (2018). "Spatial Array Thinning for Interference Cancellation under Connectivity Constraints." Web.
1. Hamed Nosrati, Elias Aboutanios, David B. Smith. Spatial Array Thinning for Interference Cancellation under Connectivity Constraints [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2558

Efficient Single/Multiple Unimodular Waveform Design With Low Weighted Correlatioins


A new method for designing single/multiple unimodular waveforms with good weighted correlation properties, which is

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1 March 2017 - 6:24pm
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ICASSP2017PosterLiVorobyov.pdf

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[1] , "Efficient Single/Multiple Unimodular Waveform Design With Low Weighted Correlatioins", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1566. Accessed: May. 30, 2020.
@article{1566-17,
url = {http://sigport.org/1566},
author = { },
publisher = {IEEE SigPort},
title = {Efficient Single/Multiple Unimodular Waveform Design With Low Weighted Correlatioins},
year = {2017} }
TY - EJOUR
T1 - Efficient Single/Multiple Unimodular Waveform Design With Low Weighted Correlatioins
AU -
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1566
ER -
. (2017). Efficient Single/Multiple Unimodular Waveform Design With Low Weighted Correlatioins. IEEE SigPort. http://sigport.org/1566
, 2017. Efficient Single/Multiple Unimodular Waveform Design With Low Weighted Correlatioins. Available at: http://sigport.org/1566.
. (2017). "Efficient Single/Multiple Unimodular Waveform Design With Low Weighted Correlatioins." Web.
1. . Efficient Single/Multiple Unimodular Waveform Design With Low Weighted Correlatioins [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1566

Approximate Support Recovery of Atomic Line Spectral Estimation: A Tale of Resolution and Precision


This work investigates the parameter estimation performance of super-resolution line spectral estimation using atomic norm minimization. The focus is on analyzing the algorithm's accuracy of inferring the frequencies and complex magnitudes from noisy observations. When the Signal-to-Noise Ratio is reasonably high and the true frequencies are separated by $O(\frac{1}{n})$, the atomic norm estimator is shown to localize the correct number of frequencies, each within a neighborhood of size $O(\sqrt{\frac{\log n}{n^3}} \sigma)$ of one of the true frequencies.

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10 December 2016 - 3:39pm
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Slides_GlobalSIP.pdf

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[1] , "Approximate Support Recovery of Atomic Line Spectral Estimation: A Tale of Resolution and Precision", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1383. Accessed: May. 30, 2020.
@article{1383-16,
url = {http://sigport.org/1383},
author = { },
publisher = {IEEE SigPort},
title = {Approximate Support Recovery of Atomic Line Spectral Estimation: A Tale of Resolution and Precision},
year = {2016} }
TY - EJOUR
T1 - Approximate Support Recovery of Atomic Line Spectral Estimation: A Tale of Resolution and Precision
AU -
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1383
ER -
. (2016). Approximate Support Recovery of Atomic Line Spectral Estimation: A Tale of Resolution and Precision. IEEE SigPort. http://sigport.org/1383
, 2016. Approximate Support Recovery of Atomic Line Spectral Estimation: A Tale of Resolution and Precision. Available at: http://sigport.org/1383.
. (2016). "Approximate Support Recovery of Atomic Line Spectral Estimation: A Tale of Resolution and Precision." Web.
1. . Approximate Support Recovery of Atomic Line Spectral Estimation: A Tale of Resolution and Precision [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1383

Approximate Support Recovery of Atomic Line Spectral Estimation: A Tale of Resolution and Precision


This work investigates the parameter estimation performance of super-resolution line spectral estimation using atomic norm minimization. The focus is on analyzing the algorithm's accuracy of inferring the frequencies and complex magnitudes from noisy observations. When the Signal-to-Noise Ratio is reasonably high and the true frequencies are separated by $O(\frac{1}{n})$, the atomic norm estimator is shown to localize the correct number of frequencies, each within a neighborhood of size $O(\sqrt{\frac{\log n}{n^3}} \sigma)$ of one of the true frequencies.

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Submitted On:
10 December 2016 - 3:38pm
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Slides_GlobalSIP.pdf

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[1] , "Approximate Support Recovery of Atomic Line Spectral Estimation: A Tale of Resolution and Precision", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1382. Accessed: May. 30, 2020.
@article{1382-16,
url = {http://sigport.org/1382},
author = { },
publisher = {IEEE SigPort},
title = {Approximate Support Recovery of Atomic Line Spectral Estimation: A Tale of Resolution and Precision},
year = {2016} }
TY - EJOUR
T1 - Approximate Support Recovery of Atomic Line Spectral Estimation: A Tale of Resolution and Precision
AU -
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1382
ER -
. (2016). Approximate Support Recovery of Atomic Line Spectral Estimation: A Tale of Resolution and Precision. IEEE SigPort. http://sigport.org/1382
, 2016. Approximate Support Recovery of Atomic Line Spectral Estimation: A Tale of Resolution and Precision. Available at: http://sigport.org/1382.
. (2016). "Approximate Support Recovery of Atomic Line Spectral Estimation: A Tale of Resolution and Precision." Web.
1. . Approximate Support Recovery of Atomic Line Spectral Estimation: A Tale of Resolution and Precision [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1382

Extension of Nested Arrays with the Fourth-Order Difference Co-Array Enhancement

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Authors:
Qing Shen, Wei Liu, Wei Cui, Siliang Wu
Submitted On:
19 March 2016 - 6:09am
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ICASSP2016.pdf

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[1] Qing Shen, Wei Liu, Wei Cui, Siliang Wu, "Extension of Nested Arrays with the Fourth-Order Difference Co-Array Enhancement", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/798. Accessed: May. 30, 2020.
@article{798-16,
url = {http://sigport.org/798},
author = {Qing Shen; Wei Liu; Wei Cui; Siliang Wu },
publisher = {IEEE SigPort},
title = {Extension of Nested Arrays with the Fourth-Order Difference Co-Array Enhancement},
year = {2016} }
TY - EJOUR
T1 - Extension of Nested Arrays with the Fourth-Order Difference Co-Array Enhancement
AU - Qing Shen; Wei Liu; Wei Cui; Siliang Wu
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/798
ER -
Qing Shen, Wei Liu, Wei Cui, Siliang Wu. (2016). Extension of Nested Arrays with the Fourth-Order Difference Co-Array Enhancement. IEEE SigPort. http://sigport.org/798
Qing Shen, Wei Liu, Wei Cui, Siliang Wu, 2016. Extension of Nested Arrays with the Fourth-Order Difference Co-Array Enhancement. Available at: http://sigport.org/798.
Qing Shen, Wei Liu, Wei Cui, Siliang Wu. (2016). "Extension of Nested Arrays with the Fourth-Order Difference Co-Array Enhancement." Web.
1. Qing Shen, Wei Liu, Wei Cui, Siliang Wu. Extension of Nested Arrays with the Fourth-Order Difference Co-Array Enhancement [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/798

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