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GlobalSIP 2019

The 7th IEEE Global Conference on Signal and Information Processing (GlobalSIP)  focuses on signal and information processing with an emphasis on up-and-coming signal processing themes. The conference features world-class plenary speeches, distinguished symposium talks, tutorials, exhibits, oral and poster sessions, and panels. GlobalSIP is comprised of co-located General Symposium and symposia selected based on responses to the call-for-symposia proposals.

Compressive Super-Pixel LiDAR for High-Framerate 3D Depth Imaging


We propose a new sampling and reconstruction framework for full frame depth imaging using synchronised, programmable laser diode and photon detector arrays. By adopting a measurement scheme that probes the environment with sparse, pseudo-random patterns, our method enables eyesafe LiDAR operation, while guaranteeing fast reconstruction of

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Authors:
Brian Stewart, Joao F.C. Mota, Andrew M. Wallace
Submitted On:
8 November 2019 - 6:40am
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SuperPixel LiDAR GlobalSIP19 print.pdf

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[1] Brian Stewart, Joao F.C. Mota, Andrew M. Wallace, "Compressive Super-Pixel LiDAR for High-Framerate 3D Depth Imaging", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4930. Accessed: Dec. 15, 2019.
@article{4930-19,
url = {http://sigport.org/4930},
author = {Brian Stewart; Joao F.C. Mota; Andrew M. Wallace },
publisher = {IEEE SigPort},
title = {Compressive Super-Pixel LiDAR for High-Framerate 3D Depth Imaging},
year = {2019} }
TY - EJOUR
T1 - Compressive Super-Pixel LiDAR for High-Framerate 3D Depth Imaging
AU - Brian Stewart; Joao F.C. Mota; Andrew M. Wallace
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4930
ER -
Brian Stewart, Joao F.C. Mota, Andrew M. Wallace. (2019). Compressive Super-Pixel LiDAR for High-Framerate 3D Depth Imaging. IEEE SigPort. http://sigport.org/4930
Brian Stewart, Joao F.C. Mota, Andrew M. Wallace, 2019. Compressive Super-Pixel LiDAR for High-Framerate 3D Depth Imaging. Available at: http://sigport.org/4930.
Brian Stewart, Joao F.C. Mota, Andrew M. Wallace. (2019). "Compressive Super-Pixel LiDAR for High-Framerate 3D Depth Imaging." Web.
1. Brian Stewart, Joao F.C. Mota, Andrew M. Wallace. Compressive Super-Pixel LiDAR for High-Framerate 3D Depth Imaging [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4930

Integrated Power and D2D Communications Simulator for Future Power Systems

Paper Details

Authors:
Kevin Shimotakahara, Medhat Elsayed, Melike Erol-Kantarci, Karin Hinzer
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7 November 2019 - 9:37pm
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GlobalSIP2019 Main.pdf

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[1] Kevin Shimotakahara, Medhat Elsayed, Melike Erol-Kantarci, Karin Hinzer, "Integrated Power and D2D Communications Simulator for Future Power Systems", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4929. Accessed: Dec. 15, 2019.
@article{4929-19,
url = {http://sigport.org/4929},
author = {Kevin Shimotakahara; Medhat Elsayed; Melike Erol-Kantarci; Karin Hinzer },
publisher = {IEEE SigPort},
title = {Integrated Power and D2D Communications Simulator for Future Power Systems},
year = {2019} }
TY - EJOUR
T1 - Integrated Power and D2D Communications Simulator for Future Power Systems
AU - Kevin Shimotakahara; Medhat Elsayed; Melike Erol-Kantarci; Karin Hinzer
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4929
ER -
Kevin Shimotakahara, Medhat Elsayed, Melike Erol-Kantarci, Karin Hinzer. (2019). Integrated Power and D2D Communications Simulator for Future Power Systems. IEEE SigPort. http://sigport.org/4929
Kevin Shimotakahara, Medhat Elsayed, Melike Erol-Kantarci, Karin Hinzer, 2019. Integrated Power and D2D Communications Simulator for Future Power Systems. Available at: http://sigport.org/4929.
Kevin Shimotakahara, Medhat Elsayed, Melike Erol-Kantarci, Karin Hinzer. (2019). "Integrated Power and D2D Communications Simulator for Future Power Systems." Web.
1. Kevin Shimotakahara, Medhat Elsayed, Melike Erol-Kantarci, Karin Hinzer. Integrated Power and D2D Communications Simulator for Future Power Systems [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4929

Deep Learning Based Mass Detection in Mammograms


Mammogram is the primary imaging technique for breast cancer screening, the leading type of cancer in women worldwide. While the clinical effectiveness of mammogram has been well demonstrated, the mammographic characteristics of breast masses are quite complex. As a result, radiologists certified for reading mammography are lacking, which limits the accessibility of mammography for more population. In this paper, we propose a Computer Aided Detection (CADe) method to automatically detect masses in mammography.

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Authors:
Zhenjie Cao, Zhicheng Yang, Yanbo Zhang, Ruei-Sung Lin, Shibin Wu, Lingyun Huang, Mei Han, Jie Ma
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7 November 2019 - 8:29pm
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Presentation Slides

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[1] Zhenjie Cao, Zhicheng Yang, Yanbo Zhang, Ruei-Sung Lin, Shibin Wu, Lingyun Huang, Mei Han, Jie Ma, "Deep Learning Based Mass Detection in Mammograms", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4928. Accessed: Dec. 15, 2019.
@article{4928-19,
url = {http://sigport.org/4928},
author = {Zhenjie Cao; Zhicheng Yang; Yanbo Zhang; Ruei-Sung Lin; Shibin Wu; Lingyun Huang; Mei Han; Jie Ma },
publisher = {IEEE SigPort},
title = {Deep Learning Based Mass Detection in Mammograms},
year = {2019} }
TY - EJOUR
T1 - Deep Learning Based Mass Detection in Mammograms
AU - Zhenjie Cao; Zhicheng Yang; Yanbo Zhang; Ruei-Sung Lin; Shibin Wu; Lingyun Huang; Mei Han; Jie Ma
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4928
ER -
Zhenjie Cao, Zhicheng Yang, Yanbo Zhang, Ruei-Sung Lin, Shibin Wu, Lingyun Huang, Mei Han, Jie Ma. (2019). Deep Learning Based Mass Detection in Mammograms. IEEE SigPort. http://sigport.org/4928
Zhenjie Cao, Zhicheng Yang, Yanbo Zhang, Ruei-Sung Lin, Shibin Wu, Lingyun Huang, Mei Han, Jie Ma, 2019. Deep Learning Based Mass Detection in Mammograms. Available at: http://sigport.org/4928.
Zhenjie Cao, Zhicheng Yang, Yanbo Zhang, Ruei-Sung Lin, Shibin Wu, Lingyun Huang, Mei Han, Jie Ma. (2019). "Deep Learning Based Mass Detection in Mammograms." Web.
1. Zhenjie Cao, Zhicheng Yang, Yanbo Zhang, Ruei-Sung Lin, Shibin Wu, Lingyun Huang, Mei Han, Jie Ma. Deep Learning Based Mass Detection in Mammograms [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4928

AMA: An Open-source Amplitude Modulation Analysis Toolkit for Signal Processing Applications


For their analysis with conventional signal processing tools, non-stationary signals are assumed to be stationary (or at least wide-sense stationary) in short intervals. While this approach allows them to be studied, it disregards the temporal evolution of their statistics. As such, to analyze this type of signals, it is desirable to use a representation that registers and characterizes the temporal changes in the frequency content of the signals, as these changes may occur in single or multiple periodic ways.

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Authors:
Raymundo Cassani, Isabela Albuquerque, Joao Monteiro, Tiago H. Falk
Submitted On:
7 November 2019 - 7:21pm
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Poster

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[1] Raymundo Cassani, Isabela Albuquerque, Joao Monteiro, Tiago H. Falk, "AMA: An Open-source Amplitude Modulation Analysis Toolkit for Signal Processing Applications", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4927. Accessed: Dec. 15, 2019.
@article{4927-19,
url = {http://sigport.org/4927},
author = {Raymundo Cassani; Isabela Albuquerque; Joao Monteiro; Tiago H. Falk },
publisher = {IEEE SigPort},
title = {AMA: An Open-source Amplitude Modulation Analysis Toolkit for Signal Processing Applications},
year = {2019} }
TY - EJOUR
T1 - AMA: An Open-source Amplitude Modulation Analysis Toolkit for Signal Processing Applications
AU - Raymundo Cassani; Isabela Albuquerque; Joao Monteiro; Tiago H. Falk
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4927
ER -
Raymundo Cassani, Isabela Albuquerque, Joao Monteiro, Tiago H. Falk. (2019). AMA: An Open-source Amplitude Modulation Analysis Toolkit for Signal Processing Applications. IEEE SigPort. http://sigport.org/4927
Raymundo Cassani, Isabela Albuquerque, Joao Monteiro, Tiago H. Falk, 2019. AMA: An Open-source Amplitude Modulation Analysis Toolkit for Signal Processing Applications. Available at: http://sigport.org/4927.
Raymundo Cassani, Isabela Albuquerque, Joao Monteiro, Tiago H. Falk. (2019). "AMA: An Open-source Amplitude Modulation Analysis Toolkit for Signal Processing Applications." Web.
1. Raymundo Cassani, Isabela Albuquerque, Joao Monteiro, Tiago H. Falk. AMA: An Open-source Amplitude Modulation Analysis Toolkit for Signal Processing Applications [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4927

A Fast Iterative Method for Removing Sparse Noise from Sparse Signals


Reconstructing a signal corrupted by impulsive noise is of high importance in several applications, including impulsive noise removal from images, audios and videos, and separating texts from images. Investigating this problem, in this paper we propose a new method to reconstruct a noise-corrupted signal where both signal and noise are sparse but in different domains. We apply our algorithm for impulsive noise (Salt-and-Pepper Noise (SPN) and Random-Valued Impulsive Noise (RVIN) removal from images and compare our results with other notable algorithms in the literature.

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Authors:
nematollah zarmehi, farokh marvasti, saeed gazor
Submitted On:
7 November 2019 - 3:48pm
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Sahar Sadrizadeh.pdf

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[1] nematollah zarmehi, farokh marvasti, saeed gazor, "A Fast Iterative Method for Removing Sparse Noise from Sparse Signals", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4926. Accessed: Dec. 15, 2019.
@article{4926-19,
url = {http://sigport.org/4926},
author = {nematollah zarmehi; farokh marvasti; saeed gazor },
publisher = {IEEE SigPort},
title = {A Fast Iterative Method for Removing Sparse Noise from Sparse Signals},
year = {2019} }
TY - EJOUR
T1 - A Fast Iterative Method for Removing Sparse Noise from Sparse Signals
AU - nematollah zarmehi; farokh marvasti; saeed gazor
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4926
ER -
nematollah zarmehi, farokh marvasti, saeed gazor. (2019). A Fast Iterative Method for Removing Sparse Noise from Sparse Signals. IEEE SigPort. http://sigport.org/4926
nematollah zarmehi, farokh marvasti, saeed gazor, 2019. A Fast Iterative Method for Removing Sparse Noise from Sparse Signals. Available at: http://sigport.org/4926.
nematollah zarmehi, farokh marvasti, saeed gazor. (2019). "A Fast Iterative Method for Removing Sparse Noise from Sparse Signals." Web.
1. nematollah zarmehi, farokh marvasti, saeed gazor. A Fast Iterative Method for Removing Sparse Noise from Sparse Signals [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4926

Statistical Analysis of Antenna Array Systems with Perturbations in Phase, Gain and Element Positions


In this paper, we statistically analyze the effect
of hardware impairments on power pattern of antenna array
systems. We consider a linear array and formulate the stochastic
beam pattern as a function of variations in phase, gain and
element positions. By deriving a closed-form expression for the
variance of the power pattern, we express how the performance
of antenna array can be degraded in each angle, allowing for
investigation of the role of each parameter in the final power

Paper Details

Authors:
Mohammad Hossein Moghaddam, Sina Rezaei Aghdam, Thomas Eriksson
Submitted On:
8 November 2019 - 10:40am
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MHM_Globalsip2019_Nov12.pdf

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[1] Mohammad Hossein Moghaddam, Sina Rezaei Aghdam, Thomas Eriksson, "Statistical Analysis of Antenna Array Systems with Perturbations in Phase, Gain and Element Positions", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4925. Accessed: Dec. 15, 2019.
@article{4925-19,
url = {http://sigport.org/4925},
author = {Mohammad Hossein Moghaddam; Sina Rezaei Aghdam; Thomas Eriksson },
publisher = {IEEE SigPort},
title = {Statistical Analysis of Antenna Array Systems with Perturbations in Phase, Gain and Element Positions},
year = {2019} }
TY - EJOUR
T1 - Statistical Analysis of Antenna Array Systems with Perturbations in Phase, Gain and Element Positions
AU - Mohammad Hossein Moghaddam; Sina Rezaei Aghdam; Thomas Eriksson
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4925
ER -
Mohammad Hossein Moghaddam, Sina Rezaei Aghdam, Thomas Eriksson. (2019). Statistical Analysis of Antenna Array Systems with Perturbations in Phase, Gain and Element Positions. IEEE SigPort. http://sigport.org/4925
Mohammad Hossein Moghaddam, Sina Rezaei Aghdam, Thomas Eriksson, 2019. Statistical Analysis of Antenna Array Systems with Perturbations in Phase, Gain and Element Positions. Available at: http://sigport.org/4925.
Mohammad Hossein Moghaddam, Sina Rezaei Aghdam, Thomas Eriksson. (2019). "Statistical Analysis of Antenna Array Systems with Perturbations in Phase, Gain and Element Positions." Web.
1. Mohammad Hossein Moghaddam, Sina Rezaei Aghdam, Thomas Eriksson. Statistical Analysis of Antenna Array Systems with Perturbations in Phase, Gain and Element Positions [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4925

SURFACE EMG-BASED HAND GESTURE RECOGNITION VIA DILATED CONVOLUTIONAL NEURAL NETWORKS


The recent evolution of Artificial Intelligence (AI) and deep learning models coupled with advancements of assistive robotic systems have shown great potential in significantly improving myoelectric control of prosthetic devices. In this regard, the paper proposes a novel deep-learning-based architecture for processing surface Electromyography (sEMG) signals to classify and recognize upper-limb hand gestures via incorporation of dilated causal convolutions.

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Authors:
Elahe Rahimian, Soheil Zabihi, Seyed Farokh Atashzar, Amir Asif, Arash Mohammadi$
Submitted On:
7 November 2019 - 10:35am
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Hand Gesture Recognition via Dilated Causal Convolutions

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[1] Elahe Rahimian, Soheil Zabihi, Seyed Farokh Atashzar, Amir Asif, Arash Mohammadi$, "SURFACE EMG-BASED HAND GESTURE RECOGNITION VIA DILATED CONVOLUTIONAL NEURAL NETWORKS", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4924. Accessed: Dec. 15, 2019.
@article{4924-19,
url = {http://sigport.org/4924},
author = {Elahe Rahimian; Soheil Zabihi; Seyed Farokh Atashzar; Amir Asif; Arash Mohammadi$ },
publisher = {IEEE SigPort},
title = {SURFACE EMG-BASED HAND GESTURE RECOGNITION VIA DILATED CONVOLUTIONAL NEURAL NETWORKS},
year = {2019} }
TY - EJOUR
T1 - SURFACE EMG-BASED HAND GESTURE RECOGNITION VIA DILATED CONVOLUTIONAL NEURAL NETWORKS
AU - Elahe Rahimian; Soheil Zabihi; Seyed Farokh Atashzar; Amir Asif; Arash Mohammadi$
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4924
ER -
Elahe Rahimian, Soheil Zabihi, Seyed Farokh Atashzar, Amir Asif, Arash Mohammadi$. (2019). SURFACE EMG-BASED HAND GESTURE RECOGNITION VIA DILATED CONVOLUTIONAL NEURAL NETWORKS. IEEE SigPort. http://sigport.org/4924
Elahe Rahimian, Soheil Zabihi, Seyed Farokh Atashzar, Amir Asif, Arash Mohammadi$, 2019. SURFACE EMG-BASED HAND GESTURE RECOGNITION VIA DILATED CONVOLUTIONAL NEURAL NETWORKS. Available at: http://sigport.org/4924.
Elahe Rahimian, Soheil Zabihi, Seyed Farokh Atashzar, Amir Asif, Arash Mohammadi$. (2019). "SURFACE EMG-BASED HAND GESTURE RECOGNITION VIA DILATED CONVOLUTIONAL NEURAL NETWORKS." Web.
1. Elahe Rahimian, Soheil Zabihi, Seyed Farokh Atashzar, Amir Asif, Arash Mohammadi$. SURFACE EMG-BASED HAND GESTURE RECOGNITION VIA DILATED CONVOLUTIONAL NEURAL NETWORKS [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4924

GMM-UBM based Person Verification using footfall signatures for Smart Home Applications


In this paper, we propose a novel person verification system based on footfall signatures using Gaussian Mixture Model-Universal Background Model (GMM-UBM). Ground vibration generated by footfall of an individual is used as a biometric modality. We conduct extensive experiments to compare the proposed technique with various baselines of footfall based person verification. The system is evaluated on an indigenous dataset containing 7750 footfall events of twenty subjects.

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Authors:
Bodhibrata Mukhpadhay, Manohar Parvatini, Subrat Kar
Submitted On:
7 November 2019 - 8:39am
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globalsip_2019.pdf

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[1] Bodhibrata Mukhpadhay, Manohar Parvatini, Subrat Kar, "GMM-UBM based Person Verification using footfall signatures for Smart Home Applications", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4923. Accessed: Dec. 15, 2019.
@article{4923-19,
url = {http://sigport.org/4923},
author = {Bodhibrata Mukhpadhay; Manohar Parvatini; Subrat Kar },
publisher = {IEEE SigPort},
title = {GMM-UBM based Person Verification using footfall signatures for Smart Home Applications},
year = {2019} }
TY - EJOUR
T1 - GMM-UBM based Person Verification using footfall signatures for Smart Home Applications
AU - Bodhibrata Mukhpadhay; Manohar Parvatini; Subrat Kar
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4923
ER -
Bodhibrata Mukhpadhay, Manohar Parvatini, Subrat Kar. (2019). GMM-UBM based Person Verification using footfall signatures for Smart Home Applications. IEEE SigPort. http://sigport.org/4923
Bodhibrata Mukhpadhay, Manohar Parvatini, Subrat Kar, 2019. GMM-UBM based Person Verification using footfall signatures for Smart Home Applications. Available at: http://sigport.org/4923.
Bodhibrata Mukhpadhay, Manohar Parvatini, Subrat Kar. (2019). "GMM-UBM based Person Verification using footfall signatures for Smart Home Applications." Web.
1. Bodhibrata Mukhpadhay, Manohar Parvatini, Subrat Kar. GMM-UBM based Person Verification using footfall signatures for Smart Home Applications [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4923

Optimized Polarization Filtering Based Self-Interference Cancellation Scheme for Full-Duplex Communication


In this paper, we propose an optimized polarization filtering based digital self-interference (SI) cancellation (OPC) scheme for full-duplex communication. The proposed OPC scheme utilizes polarization to increase the distinction between the desired signal and the SI and maximizes the signal to interference plus noise ratio (SINR) of the output signal.

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Authors:
Fengqi Bai, Fangfang Liu, Chunyan Feng
Submitted On:
7 November 2019 - 7:30am
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Optimized Polarization Filtering Based Self-Interference Cancellation Scheme for Full-Duplex Communication.pdf

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[1] Fengqi Bai, Fangfang Liu, Chunyan Feng, "Optimized Polarization Filtering Based Self-Interference Cancellation Scheme for Full-Duplex Communication", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4922. Accessed: Dec. 15, 2019.
@article{4922-19,
url = {http://sigport.org/4922},
author = {Fengqi Bai; Fangfang Liu; Chunyan Feng },
publisher = {IEEE SigPort},
title = {Optimized Polarization Filtering Based Self-Interference Cancellation Scheme for Full-Duplex Communication},
year = {2019} }
TY - EJOUR
T1 - Optimized Polarization Filtering Based Self-Interference Cancellation Scheme for Full-Duplex Communication
AU - Fengqi Bai; Fangfang Liu; Chunyan Feng
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4922
ER -
Fengqi Bai, Fangfang Liu, Chunyan Feng. (2019). Optimized Polarization Filtering Based Self-Interference Cancellation Scheme for Full-Duplex Communication. IEEE SigPort. http://sigport.org/4922
Fengqi Bai, Fangfang Liu, Chunyan Feng, 2019. Optimized Polarization Filtering Based Self-Interference Cancellation Scheme for Full-Duplex Communication. Available at: http://sigport.org/4922.
Fengqi Bai, Fangfang Liu, Chunyan Feng. (2019). "Optimized Polarization Filtering Based Self-Interference Cancellation Scheme for Full-Duplex Communication." Web.
1. Fengqi Bai, Fangfang Liu, Chunyan Feng. Optimized Polarization Filtering Based Self-Interference Cancellation Scheme for Full-Duplex Communication [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4922

Beam Alignment-Based mmWave Spectrum Sensing in Cognitive Vehicular Networks


Millimeter wave (mmWave) communication is a promising technology to alleviate the shortage of spectrum resources in vehicular networks. To use mmWave spectrum resources more efficiently, in this paper we propose a novel beam alignment-based vehicular mmWave spectrum sensing model and algorithm. We first establish the spectrum sensing model on the basis of characteristics of mmWave signals and then derive the test statistics.

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Authors:
Caili Guo
Submitted On:
11 November 2019 - 8:52pm
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#1570567800 Beam Alignment-Based mmWave Spectrum Sensing in Cognitive Vehicular Networks.pptx

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[1] Caili Guo, "Beam Alignment-Based mmWave Spectrum Sensing in Cognitive Vehicular Networks", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4918. Accessed: Dec. 15, 2019.
@article{4918-19,
url = {http://sigport.org/4918},
author = {Caili Guo },
publisher = {IEEE SigPort},
title = {Beam Alignment-Based mmWave Spectrum Sensing in Cognitive Vehicular Networks},
year = {2019} }
TY - EJOUR
T1 - Beam Alignment-Based mmWave Spectrum Sensing in Cognitive Vehicular Networks
AU - Caili Guo
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4918
ER -
Caili Guo. (2019). Beam Alignment-Based mmWave Spectrum Sensing in Cognitive Vehicular Networks. IEEE SigPort. http://sigport.org/4918
Caili Guo, 2019. Beam Alignment-Based mmWave Spectrum Sensing in Cognitive Vehicular Networks. Available at: http://sigport.org/4918.
Caili Guo. (2019). "Beam Alignment-Based mmWave Spectrum Sensing in Cognitive Vehicular Networks." Web.
1. Caili Guo. Beam Alignment-Based mmWave Spectrum Sensing in Cognitive Vehicular Networks [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4918

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