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Signal Processing for Communications and Networking

OPTIMUM DECISION FUSION IN COGNITIVE WIRELESS SENSOR NETWORKS WITH UNKNOWN USERS LOCATION


We consider a cooperative cognitive wireless network sce- nario where a primary wireless network is co-located with a cognitive (or secondary) network. In the considered scenario, the nodes of the secondary network make local binary de- cisions about the presence of a signal emitted by a primary node. Then, they transmit their decisions to a fusion center (FC). The final decision about the channel state is up to the FC by means of a proper fusion rule.

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Authors:
Andrea Abrardo
Submitted On:
23 February 2016 - 1:44pm
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[1] Andrea Abrardo, "OPTIMUM DECISION FUSION IN COGNITIVE WIRELESS SENSOR NETWORKS WITH UNKNOWN USERS LOCATION", IEEE SigPort, 2014. [Online]. Available: http://sigport.org/122. Accessed: May. 30, 2020.
@article{122-14,
url = {http://sigport.org/122},
author = {Andrea Abrardo },
publisher = {IEEE SigPort},
title = {OPTIMUM DECISION FUSION IN COGNITIVE WIRELESS SENSOR NETWORKS WITH UNKNOWN USERS LOCATION},
year = {2014} }
TY - EJOUR
T1 - OPTIMUM DECISION FUSION IN COGNITIVE WIRELESS SENSOR NETWORKS WITH UNKNOWN USERS LOCATION
AU - Andrea Abrardo
PY - 2014
PB - IEEE SigPort
UR - http://sigport.org/122
ER -
Andrea Abrardo. (2014). OPTIMUM DECISION FUSION IN COGNITIVE WIRELESS SENSOR NETWORKS WITH UNKNOWN USERS LOCATION. IEEE SigPort. http://sigport.org/122
Andrea Abrardo, 2014. OPTIMUM DECISION FUSION IN COGNITIVE WIRELESS SENSOR NETWORKS WITH UNKNOWN USERS LOCATION. Available at: http://sigport.org/122.
Andrea Abrardo. (2014). "OPTIMUM DECISION FUSION IN COGNITIVE WIRELESS SENSOR NETWORKS WITH UNKNOWN USERS LOCATION." Web.
1. Andrea Abrardo. OPTIMUM DECISION FUSION IN COGNITIVE WIRELESS SENSOR NETWORKS WITH UNKNOWN USERS LOCATION [Internet]. IEEE SigPort; 2014. Available from : http://sigport.org/122

Sparse Beamspace Equalization for Massive MU-MIMO mmWave Systems

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Authors:
S. H. Mirfarshbafan and C. Studer
Submitted On:
15 May 2020 - 6:23am
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[1] S. H. Mirfarshbafan and C. Studer, "Sparse Beamspace Equalization for Massive MU-MIMO mmWave Systems", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5346. Accessed: May. 30, 2020.
@article{5346-20,
url = {http://sigport.org/5346},
author = {S. H. Mirfarshbafan and C. Studer },
publisher = {IEEE SigPort},
title = {Sparse Beamspace Equalization for Massive MU-MIMO mmWave Systems},
year = {2020} }
TY - EJOUR
T1 - Sparse Beamspace Equalization for Massive MU-MIMO mmWave Systems
AU - S. H. Mirfarshbafan and C. Studer
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5346
ER -
S. H. Mirfarshbafan and C. Studer. (2020). Sparse Beamspace Equalization for Massive MU-MIMO mmWave Systems. IEEE SigPort. http://sigport.org/5346
S. H. Mirfarshbafan and C. Studer, 2020. Sparse Beamspace Equalization for Massive MU-MIMO mmWave Systems. Available at: http://sigport.org/5346.
S. H. Mirfarshbafan and C. Studer. (2020). "Sparse Beamspace Equalization for Massive MU-MIMO mmWave Systems." Web.
1. S. H. Mirfarshbafan and C. Studer. Sparse Beamspace Equalization for Massive MU-MIMO mmWave Systems [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5346

Joint Sparse Recovery using Deep Unfolding With Application to Massive Random Access

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14 May 2020 - 9:29pm
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[1] , "Joint Sparse Recovery using Deep Unfolding With Application to Massive Random Access", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5328. Accessed: May. 30, 2020.
@article{5328-20,
url = {http://sigport.org/5328},
author = { },
publisher = {IEEE SigPort},
title = {Joint Sparse Recovery using Deep Unfolding With Application to Massive Random Access},
year = {2020} }
TY - EJOUR
T1 - Joint Sparse Recovery using Deep Unfolding With Application to Massive Random Access
AU -
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5328
ER -
. (2020). Joint Sparse Recovery using Deep Unfolding With Application to Massive Random Access. IEEE SigPort. http://sigport.org/5328
, 2020. Joint Sparse Recovery using Deep Unfolding With Application to Massive Random Access. Available at: http://sigport.org/5328.
. (2020). "Joint Sparse Recovery using Deep Unfolding With Application to Massive Random Access." Web.
1. . Joint Sparse Recovery using Deep Unfolding With Application to Massive Random Access [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5328

Proximal Multitask Learning Over Distributed Networks with Jointly Sparse Structure

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13 May 2020 - 10:35pm
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[1] , "Proximal Multitask Learning Over Distributed Networks with Jointly Sparse Structure", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5201. Accessed: May. 30, 2020.
@article{5201-20,
url = {http://sigport.org/5201},
author = { },
publisher = {IEEE SigPort},
title = {Proximal Multitask Learning Over Distributed Networks with Jointly Sparse Structure},
year = {2020} }
TY - EJOUR
T1 - Proximal Multitask Learning Over Distributed Networks with Jointly Sparse Structure
AU -
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5201
ER -
. (2020). Proximal Multitask Learning Over Distributed Networks with Jointly Sparse Structure. IEEE SigPort. http://sigport.org/5201
, 2020. Proximal Multitask Learning Over Distributed Networks with Jointly Sparse Structure. Available at: http://sigport.org/5201.
. (2020). "Proximal Multitask Learning Over Distributed Networks with Jointly Sparse Structure." Web.
1. . Proximal Multitask Learning Over Distributed Networks with Jointly Sparse Structure [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5201

Proximal Multitask Learning Over Distributed Networks with Jointly Sparse Structure

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13 May 2020 - 10:27pm
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[1] , "Proximal Multitask Learning Over Distributed Networks with Jointly Sparse Structure", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5198. Accessed: May. 30, 2020.
@article{5198-20,
url = {http://sigport.org/5198},
author = { },
publisher = {IEEE SigPort},
title = {Proximal Multitask Learning Over Distributed Networks with Jointly Sparse Structure},
year = {2020} }
TY - EJOUR
T1 - Proximal Multitask Learning Over Distributed Networks with Jointly Sparse Structure
AU -
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5198
ER -
. (2020). Proximal Multitask Learning Over Distributed Networks with Jointly Sparse Structure. IEEE SigPort. http://sigport.org/5198
, 2020. Proximal Multitask Learning Over Distributed Networks with Jointly Sparse Structure. Available at: http://sigport.org/5198.
. (2020). "Proximal Multitask Learning Over Distributed Networks with Jointly Sparse Structure." Web.
1. . Proximal Multitask Learning Over Distributed Networks with Jointly Sparse Structure [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5198

A LEARNING APPROACH TO COOPERATIVE COMMUNICATION SYSTEM DESIGN


The cooperative relay network is a type of multi-terminal communication system. We present in this paper a Neural Network (NN)-based autoencoder (AE) approach to optimize its design. This approach implements a classical three-node cooperative system as one AE model, and uses a two-stage scheme to train this model and minimize the designed losses. We demonstrate that this approach shows performance close to the best baseline in decode-and-forward (DF), and outperforms the best baseline in amplify-and-forward (AF), over a wide range of signal-to-noise-ratio (SNR) values.

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Authors:
Peng Cheng, Zhuo Chen, Wai Ho Mow, Yonghui Li
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13 May 2020 - 9:33pm
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[1] Peng Cheng, Zhuo Chen, Wai Ho Mow, Yonghui Li, "A LEARNING APPROACH TO COOPERATIVE COMMUNICATION SYSTEM DESIGN", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5180. Accessed: May. 30, 2020.
@article{5180-20,
url = {http://sigport.org/5180},
author = {Peng Cheng; Zhuo Chen; Wai Ho Mow; Yonghui Li },
publisher = {IEEE SigPort},
title = {A LEARNING APPROACH TO COOPERATIVE COMMUNICATION SYSTEM DESIGN},
year = {2020} }
TY - EJOUR
T1 - A LEARNING APPROACH TO COOPERATIVE COMMUNICATION SYSTEM DESIGN
AU - Peng Cheng; Zhuo Chen; Wai Ho Mow; Yonghui Li
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5180
ER -
Peng Cheng, Zhuo Chen, Wai Ho Mow, Yonghui Li. (2020). A LEARNING APPROACH TO COOPERATIVE COMMUNICATION SYSTEM DESIGN. IEEE SigPort. http://sigport.org/5180
Peng Cheng, Zhuo Chen, Wai Ho Mow, Yonghui Li, 2020. A LEARNING APPROACH TO COOPERATIVE COMMUNICATION SYSTEM DESIGN. Available at: http://sigport.org/5180.
Peng Cheng, Zhuo Chen, Wai Ho Mow, Yonghui Li. (2020). "A LEARNING APPROACH TO COOPERATIVE COMMUNICATION SYSTEM DESIGN." Web.
1. Peng Cheng, Zhuo Chen, Wai Ho Mow, Yonghui Li. A LEARNING APPROACH TO COOPERATIVE COMMUNICATION SYSTEM DESIGN [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5180

Robust Hybrid Beamforming for Satellite-Terrestrial Integrated Networks

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Authors:
Zhi Lin, Min Lin, Benoit Champagne, Wei-Ping Zhu, Naofal Al-Dhahir
Submitted On:
13 May 2020 - 8:24pm
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[1] Zhi Lin, Min Lin, Benoit Champagne, Wei-Ping Zhu, Naofal Al-Dhahir, "Robust Hybrid Beamforming for Satellite-Terrestrial Integrated Networks", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5169. Accessed: May. 30, 2020.
@article{5169-20,
url = {http://sigport.org/5169},
author = {Zhi Lin; Min Lin; Benoit Champagne; Wei-Ping Zhu; Naofal Al-Dhahir },
publisher = {IEEE SigPort},
title = {Robust Hybrid Beamforming for Satellite-Terrestrial Integrated Networks},
year = {2020} }
TY - EJOUR
T1 - Robust Hybrid Beamforming for Satellite-Terrestrial Integrated Networks
AU - Zhi Lin; Min Lin; Benoit Champagne; Wei-Ping Zhu; Naofal Al-Dhahir
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5169
ER -
Zhi Lin, Min Lin, Benoit Champagne, Wei-Ping Zhu, Naofal Al-Dhahir. (2020). Robust Hybrid Beamforming for Satellite-Terrestrial Integrated Networks. IEEE SigPort. http://sigport.org/5169
Zhi Lin, Min Lin, Benoit Champagne, Wei-Ping Zhu, Naofal Al-Dhahir, 2020. Robust Hybrid Beamforming for Satellite-Terrestrial Integrated Networks. Available at: http://sigport.org/5169.
Zhi Lin, Min Lin, Benoit Champagne, Wei-Ping Zhu, Naofal Al-Dhahir. (2020). "Robust Hybrid Beamforming for Satellite-Terrestrial Integrated Networks." Web.
1. Zhi Lin, Min Lin, Benoit Champagne, Wei-Ping Zhu, Naofal Al-Dhahir. Robust Hybrid Beamforming for Satellite-Terrestrial Integrated Networks [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5169

Information Flow Optimization in Inference Networks


The problem of maximizing the information flow through a sensor network tasked with an inference objective at the fusion center is considered. The sensor nodes take observations, compress, and send them to the fusion center through a network of relays. The network imposes capacity constraints on the rate of transmission in each connection and flow conservation constraints. It is shown that this rate-constrained inference problem can be cast as a Network Utility Maximization problem by suitably defining the utility functions for each sensor, and can be solved using existing techniques.

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Authors:
Jing Liu, Venugopal Veeravalli, Gunjan Verma
Submitted On:
13 May 2020 - 5:16pm
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[1] Jing Liu, Venugopal Veeravalli, Gunjan Verma, "Information Flow Optimization in Inference Networks", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5147. Accessed: May. 30, 2020.
@article{5147-20,
url = {http://sigport.org/5147},
author = {Jing Liu; Venugopal Veeravalli; Gunjan Verma },
publisher = {IEEE SigPort},
title = {Information Flow Optimization in Inference Networks},
year = {2020} }
TY - EJOUR
T1 - Information Flow Optimization in Inference Networks
AU - Jing Liu; Venugopal Veeravalli; Gunjan Verma
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5147
ER -
Jing Liu, Venugopal Veeravalli, Gunjan Verma. (2020). Information Flow Optimization in Inference Networks. IEEE SigPort. http://sigport.org/5147
Jing Liu, Venugopal Veeravalli, Gunjan Verma, 2020. Information Flow Optimization in Inference Networks. Available at: http://sigport.org/5147.
Jing Liu, Venugopal Veeravalli, Gunjan Verma. (2020). "Information Flow Optimization in Inference Networks." Web.
1. Jing Liu, Venugopal Veeravalli, Gunjan Verma. Information Flow Optimization in Inference Networks [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5147

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: May. 30, 2020.
@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

Provably Accelerated Randomized Gossip Algorithms


In this work we present novel provably accelerated gossip algorithms for solving the average consensus problem. The proposed protocols are inspired from the recently developed accelerated variants of the randomized Kaczmarz method - a popular method for solving linear systems. In each gossip iteration all nodes of the network update their values but only a pair of them exchange their private information. Numerical experiments on popular wireless sensor networks showing the benefits of our protocols are also presented.

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Authors:
Nicolas Loizou, Michael Rabbat, Peter Richtarik
Submitted On:
9 May 2019 - 4:21pm
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AccGossipPoster.pdf

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[1] Nicolas Loizou, Michael Rabbat, Peter Richtarik, "Provably Accelerated Randomized Gossip Algorithms", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4237. Accessed: May. 30, 2020.
@article{4237-19,
url = {http://sigport.org/4237},
author = {Nicolas Loizou; Michael Rabbat; Peter Richtarik },
publisher = {IEEE SigPort},
title = {Provably Accelerated Randomized Gossip Algorithms},
year = {2019} }
TY - EJOUR
T1 - Provably Accelerated Randomized Gossip Algorithms
AU - Nicolas Loizou; Michael Rabbat; Peter Richtarik
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4237
ER -
Nicolas Loizou, Michael Rabbat, Peter Richtarik. (2019). Provably Accelerated Randomized Gossip Algorithms. IEEE SigPort. http://sigport.org/4237
Nicolas Loizou, Michael Rabbat, Peter Richtarik, 2019. Provably Accelerated Randomized Gossip Algorithms. Available at: http://sigport.org/4237.
Nicolas Loizou, Michael Rabbat, Peter Richtarik. (2019). "Provably Accelerated Randomized Gossip Algorithms." Web.
1. Nicolas Loizou, Michael Rabbat, Peter Richtarik. Provably Accelerated Randomized Gossip Algorithms [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4237

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